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12 ideas for better data visualization

Posted on by Imarticus Learning

What is Data Visualization

Data visualization is an integral part of any business. It helps you see your data in a new way and makes it easier to understand trends and patterns. This blog post will discuss different ideas for better data visualization with Power BI. These tips will help you learn data visualization, create more effective charts and graphs and make your data more understandable to your customers.

Here are nine tips for better data visualization:

1. Use the correct chart for your data

There are many different types of charts and graphs available, and it can be challenging to know which one to use. When choosing a chart, ensure that it is the best type for your data. A bar chart or column chart will be a good choice if you have categorical data. A line graph or scatter plot would be better if you have numerical data.

2. Use colors to your advantage

Colors can be beneficial when it comes to data visualization. They can help highlight certain aspects of the data and make the overall visualization more aesthetically pleasing. However, it is essential to use colors wisely. 

3. Keep it simple

Complex visualizations can be challenging to understand and take time to create. Simplifying your visualizations can make them more effective and easier to interpret. 

4. Use labels and annotations

Labels and annotations can be beneficial in data visualizations. They can help explain the data and provide context for the viewer. 

5. Tell a story

A data visualization should tell a story. It should always have a beginning, middle, and end. The beginning should introduce the viewer to the data and the problem you are trying to solve. The center should effectively present the data. And the end should provide a conclusion or call to action. 

6. Choose the most compelling vision.

There are many ways to visualize data. But not all visualizations are equally compelling. When choosing a visualization, consider what type of data you have and what you want to communicate. 

7. Combine shape indicators

Indicators are a great way to show data, but they can be even more effective when combined with shapes. You can create informative and visually appealing visualizations using both shape and color.

8. Use icons or images

Icons and images can be a great way to add context to your data visualizations. They can help explain the data and make the overall visualization more visually appealing. 

9. Think about the layout

The layout of your data visualization is essential. It should also be easy to understand and interpret. The overall design should be uncluttered and straightforward. You can ensure that your visualization is practical and easy to understand by thinking about the layout.

By following these tips, you can create practical and visually appealing visualizations.

Discover data analytics courses with Imarticus Learning

This data analytics course with placement assurance is to help students learn data visualization and how to apply Data Science in the real world and create complex models that produce essential business insights and forecasts.

Course Benefits for Learners:

  • To be employable, students must have a firm grasp of what is data visualization, data analytics, machine learning basics, and the most in-demand data science tools and techniques.
  • Learners earn a tableau certification by participating in 25 real-world projects and case studies led by business partners.
  • Data science and analytics jobs are among the most in-demand skills in today’s workplace, so recent graduates and early-career professionals should consider enrolling in data science and analytics programs.

Contact us through chat support, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon.

The biggest Power BI trends of 2022-2023

Posted on by Imarticus Learning

The Biggest Power BI Trends Of 2022-2023

Business Intelligence (BI) had slower progress in the recent past when only the leading companies had access to big data. It was unreachable for the majority of businesses until the pandemic. Now, not only is Power BI accessible to all kinds of businesses, but it also holds a central role in them. Since the data flow is constantly on the rise, it is necessary to have the right BI tools and experts to handle it. This is exactly what the coming trends are going to be for the year 2022-2023.

Biggest upcoming Power BI trends

Data has a higher power over businesses. It will need quality tools to gather the necessary data, sift through to find the most relevant data, analyze it properly, interpret them into a conceivable plan. There are future trends in BI for the upcoming year. The most important ones are discussed here. 

  • Data Quality Management 

Quality of data was not of much importance in the past but in the coming year, this is going to be one of the most important trends. Good quality data with correct, consistent, wholesome, and up-to-date information will be gold. There needs to be constant monitoring and management of this part that helps businesses gain the right insight. The data quality management team can then deliver useful data. 

  • Data-driven decision making

Decision-making is one of the last stages in BI utilization but has more significance in this competitive world. What drives in this direction is quality data and the equally competent interpretation of the data that will be relevant and beneficial for the business. This process will need the assistance of analytics who can decode the information that helps make easy and useful decisions. 

  • Data governance

Data Governance has always been one of the most important features in Power BI. It is the base of the data analysis on which businesses frame their management process in dealing with the data, right from its procurement, management, analysis, and its implementation. It is also important to have such a blueprint regarding data to support the effective usage of the various BI tools that enhance the efficacy of analytics. The various data analysis programs such as the Post Graduate Program in Data Analytics and Machine Learning from Imarticus can help understand this. 

  • Data discovery and visualization 

As useful as big data is, the relevant insights need to be extracted from its raw form. These collected insights will then need to be transformed into a visual form for easy understanding. Such smart discovery and visualization of data is the biggest decisive factor for the successful usage of BI. Without the proper documentation of data, BI cannot process them effectively. 

  • Self-service BI

The Self Service Business Intelligence (SSBI) features in Power BI aim for business users to be able to handle the data without much reliance on data scientists. It empowers anyone with basic knowledge about data to be able to sort, filter, and analyze data. It will lead to a huge demand for data analysts. From the business perspective, this will be a smarter move to reduce expenses and invest in the right resources. 

This in turn increases the demand for the Data Analytics course with a placement that will be of assistance even after its completion. The data analytics course with Imarticus is a good example that will provide the support of mentorship and find meaningful connections beyond the class. 

Bottom Line

To survive the upcoming Power BI trends, businesses must have employees with strong data literacy to handle the data. They also need a quality workflow that ensures competent data governance for the data-driven future.

FAQs –

  1. What are the latest trends in Power BI?

Power BI’s most recent trends include improvements in data storytelling capabilities, AI-driven analytics, better integration with other Microsoft tools, and a stronger emphasis on data security and governance. Furthermore, augmented analytics and the application of natural language processing to streamline data querying are becoming more and more important.

  1. How is AI being integrated into Power BI?

AI is being integrated into Power BI with features such as AI-powered visualizations, automated insights, and machine learning capabilities. With the role of AI, users can discover hidden patterns, predict results, and make data-driven decisions with greater efficiency. Cognitive services and AI insights are becoming more prevalent, enabling more advanced data analysis.

  1. What is augmented analytics in Power BI, and why is it important?

In Power BI, augmented analytics refers to the use of artificial intelligence and machine learning to automate data preparation, insight generation, and explanation. It is significant because it streamlines the data analysis process, making advanced analytics available to non-technical users and allowing for faster, more accurate decision-making.

  1. What role do custom visuals play in Power BI’s latest trends?

Custom visuals play an important role in Power BI’s latest trends because they enable users to create unique and tailored visualizations that meet specific business requirements. The ability to create and integrate custom visuals improves the overall data analysis experience and allows for more impactful data presentations.

  1. How can businesses benefit from the latest Power BI trends?

Businesses can benefit from the most recent Power BI trends by achieving deeper insights through advanced analytics, improving decision-making with AI-powered features, improving collaboration with seamless integration, and ensuring data security with improved governance features. These trends help businesses remain competitive and make informed decisions based on real-time data analysis.

  1. Is working in big data and analytics a good career choice?

Data analyticis is one of the most promising careers, with numerous opportunities available. Organizations today are eager to hire Data Analysts, Data Engineers, and Big Data experts. Imarticus Learning’s data science course provides the most sought-after combination of industry experts and promising placement assurance.

The Impact of Data Analytics on Customer Experience and Engagement

Posted on by Imarticus Learning

Data is king in the modern digital world, and organizations can increasingly use data-driven choices to enhance customer experience and engagement. Businesses now have unparalleled visibility into consumer behavior and preferences because of the acquisition and analysis of customer data.

By leveraging data analytics, companies may create individualized and targeted experiences that improve customer pleasure, loyalty, and revenue. In this blog, we’ll look at the impact of Data analytics on consumer engagement and experience and how organizations use this technology to stay competitive. 

So grab a seatbelt and get ready to learn how data analytics has the extraordinary potential to alter consumer experiences.

What is the impact of data analytics on customer experience?

Data analytics is a potent tool for businesses to understand their customers better. It can help you understand your business better, too. When implemented properly, data analytics can also improve employee performance and productivity.

According to a Forbes Insights survey of 357 executives from large organizations, the benefits of moving toward data-driven customer experiences (data-driven CX) include enhancing revenue generation, enabling cost reduction, and hastening process efficiency and quality improvements. 

The term “data analytics lifecycle” refers to the sequence of steps and stages that must be completed to evaluate and derive meaning from unstructured data. Steps in the lifecycle include gathering, preparing, modeling, analyzing, and visualizing data.

How can data analytics improve customer experience?

In summary, data-driven decision-making means using data analytics to make better decisions. Data analytics can help you make better choices and adapt your product or service to meet customers’ needs.

Customer experience analytics is becoming necessary for organizations looking to improve customer experience and engagement through improved targeting, personalization, and targeted offers.

Data pertaining to sales numbers, market research, logistics, linguistics, or other behaviors are organized by a data analyst. They use their technological know-how to guarantee the quality and accuracy of the data. Then, it is processed, organized, and presented in a way that supports improved decision-making by people, companies, and organizations.

  • Data extraction from primary and secondary sources using automated technology.
  • Eliminating corrupt data, resolving code issues, and other issues.
  • Rearranging data into a legible format is necessary to develop and maintain databases and data systems.
  • To determine the worth and quality of data through evaluation.
  • To find and correct coding problems, examine reports and performance metrics.
  • Finding, analyzing, and interpreting patterns and trends in massive data sets that might help with diagnosis using statistical methods

Benefits of Data Analysis for Improved Customer Experience

When it comes to managing customer experience and engagement, data analytics is key. 

  • With the right insights into your customers’ needs, you can build a better product that meets their wants and needs. 
  • This will lead to improved communication and engagement with your customers, boosting loyalty by giving them more reasons to stay loyal. 
  • It enables you to identify opportunities for growth through targeted marketing campaigns that generate revenue for your business.

Numerous approaches exist for firms to use data analytics to enhance consumer engagement and experience, including:

  • Recognizing consumer requirements and preferences: Businesses may use data analytics to segment their customers depending on their demographics, behavior, and feedback. 
  • Improving customer loyalty and retention: Data analytics may assist firms in measuring and enhancing their clients’ loyalty and satisfaction. 
  • Improving customer journey and touchpoints: Businesses may map and improve their customer journey and touchpoints across various channels and platforms using data analytics. 

What are the different Data Analytics Tools?

Data analytics is a critical component of customer experience and engagement. Customer-centric data analytics can track marketing campaigns’ success, improve operations’ efficiency, and improve product development. 

The following tools help you leverage your data:

  • Customer Relationship Management (CRM) Software – CRM software manages customer relationships across all touchpoints, including email, phone calls, and online interactions. Your CRM system must have robust reporting capabilities so you can quickly see how well each channel is performing against its goals or KPIs (key performance indicators).
  • Social Media Analytics – Social media platforms provide unprecedented access to how people interact with brands on social media channels like Facebook or Instagram. However, these platforms don’t offer many analytics options beyond basic sentiment analysis, which doesn’t provide enough information about what customers say about their experiences with your company when interacting through these channels.
  • Web Analytics – Web analytics tools give you insight into where visitors come from within your site and what activities they do once they are there – allowing you to optimize advertising campaigns accordingly.
  • Sentiment Analysis – Sentiment analysis helps determine whether users feel positively toward something by analyzing text content found within written messages posted online, such as comments left under blog posts or tweets.

The Final Words

How organizations communicate with their consumers is being revolutionized by data analytics. Companies may learn important insights about consumer behavior, tastes, and requirements by utilizing the power of data. With this data, companies can target their marketing efforts and customize their services to provide a wonderful consumer experience.

It isn’t easy to overstate the influence of data analytics on customer experience and engagement. Businesses can better understand their clients, anticipate their requirements, and deliver services that meet their expectations. Customers feel appreciated as a consequence, and brand loyalty rises.

Companies that do not use data analytics risk slipping behind their rivals in this data-driven environment. Businesses may gain a competitive advantage and spur development by investing in data analytics tools and tactics. 

Enroll in the excellent Data Science and Analytics course that Imarticus Learning is offering. With the help of this extensive curriculum, you’ll have the practical knowledge and abilities to use data science in everyday situations and build analytical models that boost corporate performance.

6 Pros And Cons Of Tableau Data Extracts V/S Live Connections

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The main goal of Data Analytics and Visualisation is extracting insights from raw data. You have to connect and interact with data from many sources to derive meaningful results. Businesses worldwide use a tool like Tableau to get this job done. One of the most powerful tools in this domain, Tableau, offers two ways to interact with data. To learn more about this domain, you can always pick a data science course that can boost your career in data science to new heights.

Join us as we unveil the meaning of these two methods. By navigating through the pros and cons of each method, you will realise the true potential of data visualisation.

Let’s get started!

What are Data Extracts?

Simply put, Tableau creates a condensed version of data called data extracts. They are stored separately from the source. These data extracts are minor in size as they only contain data required for visualisation.
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Pros of Data Extracts

Flexible Data Modelling: You get more power to customise data extracts. Through this mechanism, you can perform the following actions.

Creating calculated fields

Performing custom calculations

Defining hierarchies within the extract

Enhanced Security and Data Governance: Extracts provide a layer of security for your data. Through its encryption, you can protect your data with a password while sharing it with external parties. This capability helps you in cases where you are strictly expected to comply with privacy regulations.

Local availability: Data extracts can be locally stored on your system. You can work on them without connecting to the source. Data extracts provide flexibility as you can manipulate and shape data without an internet connection.

Cons of Data Extracts

Data Latency: You can expect data latency as the extracts are locally available. You will have to refresh the extracts to reflect the latest information in your insights and visualisations. This results in a significant delay between when data was changed in the source and when it was updated in the extract

Complex Extract Creation: Creating extracts requires more steps than a live connection. Beginners might need help to perform the following tasks for extract creation.

Defining data connection settings

Selecting data fields

Configuring filters

Managing data refresh schedules

Data Duplication: Extracts are stored locally. If you don’t refresh them frequently, discrepancies could occur with the source. This leads to confusion and unreliable analysis.

What are Live Connections?

Unlike data extracts, live connections are dynamic. Through them, you can interact with data in real-time. When you establish a live connection, Tableau retrieves data from the source whenever you interact with the visualisation.

Pros of Live Connections

Reliable Analysis: The generated analysis is trustworthy as you always work with real-time data from the source. Any changes in the source are instantly reflected in the visualisation.

Centralised Data Governance: Tableau establishes a direct connection to the data source. Tableau enforces the access controls and security protocols of the source. Thus, you can maintain centralised security protocols and governance.

Real-time Collaboration: Live connections allow multiple users to work with the same data source. They can access and interact with the visualisations in real-time.

Cons of Live Connections


Strain on Data Source: You should use a data source sparingly. This can strain the source because of repeated requests. Others accessing the same source could observe decreased performance.

Compatibility Concerns: Tableau and the data source must run on the latest updates. The versions they are operating on must be compatible with each other. Any update on Tableau requires changes in the data source as well.

Limited Data Shaping: Unlike extracts, live connections do not allow customisations. You may have to perform calculations and transformations in the source before visualising in Tableau.

Learn more from a Data Science Course. 

If your business requires insights from real-time data, work with “live connections.” However, “data extract” is your go-to mechanism if offline availability is your priority. Learn more about it by taking a Data Science course. Imarticus Learning offers many courses from top educators to make you job-ready. For any inquiries, connect with us through chat support on our Contact Us page. Our training centers are conveniently located in Mumbai, Thane, Chennai, Pune, Bengaluru, Delhi, Gurgaon, and Ahmedabad.

Frequently Asked Questions

Why is Tableau used in data science?

Tableau stands out as a leading tool in business intelligence (BI), aiding users in crafting various visual representations such as charts, graphs, maps, dashboards, and stories. With a rich set of distinctive and compelling features, Tableau has earned its popularity by empowering users to visualise and analyse data, facilitating informed business decision-making effectively.

Tableau is suitable for what kind of data?

Renowned as the forefront solution in contemporary business intelligence, the Tableau platform seamlessly extracts data from diverse sources and transforms it into actionable insights swiftly and effortlessly.

What topics are involved in the data science course?

A beginner-level Data Science course syllabus encompasses fundamental and advanced principles in data analytics, machine learning, statistics, and programming languages like Python or R.

Why Learning Python Is Essential For Data Science

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Python is a versatile programming language. When you learn Python, the programming language makes you competent for several job roles.

It finds applications in Data Science, Web Development, Analytics, Automation, Scripting, and Game Development.

Why is it so widely used?

Let’s explore the reasons to learn Python in the following sections.

What are the technical requirements of the Data Science industry?

Data Science and Analytics industry is based on mathematics and statistics. As a Data Scientist, technically, you will need some assistance to complete your job. These are listed below.

  • Fundamentals: You must know and learn Python programming language. Additionally, SQL is essential for understanding querying and database management.
  • Mathematics and Statistics: Creating reliable models is the main responsibility of a Data Scientist. This can only be done with sound knowledge of probability, regression analysis, hypothesis testing, and linear algebra.
  • Data Analysis and Manipulation: A Data Scientist performs data manipulation and analysis regularly. The programming language used for this job must help with feature engineering and data cleaning.
  • Big Data Technologies: Along with the fundamentals, you must learn frameworks. Apache Hadoop, Dask, or Apache Spark frameworks efficiently analyze and process massive databases.
  • Machine Learning: A Data Scientist must know machine learning algorithms. They help in performing regression, clustering, and classification.

Why should Data Scientists learn Python over other languages?

Data Analytics Course

When you learn Python for Data Science and Analytics, each lesson will speak of the language’s relevance in the field.

Listed below are the reasons that make Python important for Data Scientists.

If you’re wondering the reason for choosing Python over other languages, keep reading.

Simplicity: When compared with C, C++, or C#, Python is easy to learn. Data Scientists can learn the language easily and start coding in no time. The syntax of Python is simple and almost in the English language.

This enables you to focus on problem-solving instead of bothering yourself with the format of a new language. It does not just help you with the process, but it also enables teamwork. This means you can collaborate with others to complete a project on time.

Library: Python is popular for its vast library. When you talk about Data Science, the language offers one of the largest collections of frameworks and libraries. These are specific to Data Science and Analytics. For example, NumPy, Matplotlib, and Pandas are important libraries.

Support for Artificial Intelligence (AI): Tensorflow is one of the most useful libraries in Python for AI. The language also offers other frameworks and libraries. You can use these for building and training complex models. With Python, you can also integrate machine learning pipelines directly into the production systems.

Integration: In the previous section, you must’ve learned the importance of Big Data Technologies, SQL, and Visualization Tools. Python is compatible with SQL, Hadoop, and Tableau to enhance your capabilities as a Data Scientist. You can also integrate your Python code with other programming languages.

These reasons compel professionals to learn Python for Data Science and Analytics. However, you must note that programs like R, MATLAB, and Julia also help in Data Science. But, the simplicity of Python and its vast library has made it the top choice of professionals.

Join a job-assured Postgraduate Data Science & Analytics Course

As Python is lightweight, with extensive libraries, and customer support, the demand for this program is not getting down anytime sooner. When you become a data science expert, you must have a clear understanding of the fundamentals. Strengthen your concepts of mathematics and statistics and learn machine learning.

A Postgraduate Data Science and Analytics Course will direct your career in the right direction. Imarticus Learning presents a job-centric curriculum to make the most of your investment. You will also get assistance for participating in Hackathons that add value to your resume. Get ahead of your competitors by starting your journey today!

Data Cleaning and Preprocessing: Ensuring Data Quality

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Data cleaning and preprocessing are crucial phases in data analysis that entail changing raw data into a more intelligible, usable, and efficient format. Data cleaning is repairing or deleting inaccurate, corrupted, improperly formatted, duplicate, or incomplete data inside a dataset. On the other hand, data preprocessing comprises adding missing data and correcting, fixing, or eliminating inaccurate or unnecessary data from a dataset. Enrolling in a comprehensive data science course with placement assistance helps one to enhance Power BI or Python programming skills and establish a successful career in data analytics.

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By spending time and effort in data cleaning and preprocessing, firms can lower the risk of making wrong judgements based on faulty data. This ensures that their analyses and models are based on accurate and trustworthy information. Let’s get detailed insights from this blog.

Role in ensuring data quality and accuracy

Ensuring data quality and accuracy is critical for enterprises to make informed decisions and prevent costly mistakes. Here are several methods and recommended practices to maintain data quality:

  • Identify data quality aspects: Data quality is judged based on factors such as correctness, completeness, consistency, reliability, and if it’s up to date.
  • Assign data stewards: Data stewards are responsible for ensuring the data accuracy and quality on stated data sets.
  • Management of incoming data: Inaccurate data usually comes through data receiving. Thus, it’s essential to have complete data profiling and surveillance.
  • Gather correct info requirements: Satisfying the needs and providing the data to customers and users for the purpose the data is meant is a crucial component of having good data quality.
  • Monitor and analyse data quality: Continuously watching and assessing data quality is essential to ensure it fits the organisation’s needs and is correct and trustworthy.
  • Use data quality control tools: Different tools are available to monitor and measure the quality of data that users input into corporate systems.

Identifying and handling missing data

Identifying irregular data patterns and discrepancies is a crucial part of data cleaning. Inconsistent data can impede pivot tables, machine learning models, and specialised calculations. Here are some tips for identifying and correcting inconsistent data:

  • To make it simple to spot the incorrect values, use a filter that displays all of the distinct values in a column.
  • Find patterns or anomalies in the data that can point to errors or inconsistencies.
  • Find the cause of the inconsistencies, which needs more investigation or source validation.
  • Create and implement plans to address any disparities and prevent them in the future.

Inaccuracies in data collection, measurement, research design, replication, statistical analysis, analytical decisions, citation bias, publication, and other factors can all lead to inconsistent results. It is crucial to correctly analyse and compare data from various sources to find contradictions.

Techniques for identifying missing data

Here are some techniques for identifying missing data:

  • Check for null or NaN (Not a Number) values in the dataset.
  • Look for trends in the missing data, such as missing values in specific columns or rows.
  • Use summary statistics to locate missing data, such as the count of non-null values in each column.
  • Visualise the data to discover missing deals, such as heatmaps or scatterplots.
  • Use data cleansing and management techniques, such as Stata’s mvdecode function, to locate missing data.
  • Discuss how to address missing data with those who will undertake data analysis.

Benefits and limitations of automation in data cleaning processes

Benefits of automation in data cleaning processes:

  • Efficiency: Automation can minimise the burden and save time since cleaning can be time-consuming and unpleasant.
  • Consistency: Automated data cleaning assures reliable findings by applying the same cleaning techniques across all data sets.
  • Scalability: Automated data cleansing can handle massive amounts of data and be scaled up or down as needed.
  • Accuracy: Automation can decrease human error by swiftly finding and rectifying problems using automated data cleansing. Minimising human participation in data-collecting procedures ensures that data is inherently more high-quality and error-free.
  • Real-time insights: Automation can deliver real-time insights and more accurate analytics.

Limitations of automation in data cleaning processes:

  • Lack of control and transparency: Automated data cleaning methods could have various disadvantages, such as the lack of control and transparency when depending on black-box algorithms and established rules.
  • Not all data issues can be resolved automatically: User intervention can still be essential.
  • Over-reliance on automation can be a restriction, as automated solutions are not meant to replace human supervision.
  • Expensive tooling: A drawback of automated cleaning is that the right equipment could be costly.

Overview of tools and software for data cleaning and preprocessing

Data scientists are estimated to spend 80 to 90 % of their time cleaning data. Numerous industry solutions are accessible to speed up data cleansing, which can be valuable for beginners. Here are some of the best data-cleaning tools and software:

  • OpenRefine: A user-friendly GUI (graphical user interface) application that allows users to investigate and tidy data effortlessly without programming.
  • Trifacta: A data preparation tool that provides a visual interface for cleaning and manipulating data.
  • Tibco Clarity: A data quality tool that can assist in finding and rectifying data mistakes and inconsistencies.
  • RingLead: A data purification tool that can assist in finding and removing duplicates in the data.
  • Talend: An open-source data integration tool that can aid with data cleansing and preparation.
  • Paxata:  A self-service data preparation tool that can help automate data cleansing activities.
  • Cloudingo: A data purification tool that can assist in finding and eliminating duplicates in the data.
  • Tableau Prep: A data preparation tool that gives visible and direct ways to integrate and clean the data.

How to ensure data quality in data cleaning and preprocessing?

Here are some steps to ensure data quality in data cleaning and preprocessing:

  • Monitor mistakes and maintain a record of patterns where most errors come from.
  • Use automated regression testing with detailed data comparisons to ensure excellent data quality consistently.
  • Cross-check matching data points and ensure the data is regularly formatted and suitably clean for needs.
  • Normalise the data by putting it into a language that computers can comprehend for optimal analysis.

Conclusion

Data cleaning and preprocessing are crucial in the significant data era, as businesses acquire and analyse massive volumes of data from various sources. The demand for efficient data cleaning and preprocessing methods has expanded along with data available from multiple sources, including social media, IoT devices, and online transactions.

Imarticus Learning offers a Postgraduate Program in Data Science and Analytics designed for recent graduates and professionals who want to develop a successful career in data analytics. This data science course with placement covers several topics, including Python programming, SQL, Data Analytics, Machine Learning, Power BI, and Tableau. The machine learning certification course aims to educate students with the skills and information they need to become data analysts and work in data science. Check the website for further details.

How To Become A Data Analyst With A Job Assistance Program

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How To Become A Data Analyst With A Job Assistance Program

A career in data analysis is developing into a boom since most companies today rely heavily on it. Be it for marketing strategies or other problem areas of the business, every organization can use a good data analyst. Therefore, the market is a thriving one. 

Remember that companies look for people who they can rely on, and thus you must have credible qualifications and degrees. The best way to look for a data analyst job opportunities is to take up a data analytics certification course 

What is Data Analytics? 

Data analytics is the process of extracting useful information from a body of unintelligible raw data. This processed data is then used to make decisions and solve problems an organization might face. The large body of data helps analysts draw theories that can later be worked upon. It helps several departments and gives the owners and the stakeholders the idea about their business. 

The use of data is done differently by different industries. A bank or a financial institution might use it to better customer relations, whereas a medical facility may use its data to predict future needs. Likewise, different fields sieve out important information. Then they work on the data that will help them understand their priorities the best. 

Every company today is reliant on data, and thus they keep looking for the best analysts. Data analysts are becoming more and more indispensable for businesses today. Being a data analyst will help your career grow upwards. 

Why Take a Data Analytics Course?

Data analytics is the future, and it has made its impression in the present as well. Having skills in this field will help your career go a long way. With every industry becoming reliant on data analytics, job opportunities are expanding. However, to get job opportunities in a prestigious company, one must have the required skills. So here are some reasons why you should learn data analytics and how it will help your career. 

Job Opportunity 

Job opportunity is the first and foremost reason to enrol in a data analytics program. Several students today are focusing on data analytics and looking towards making a career in the same. Immense job opportunities in data analytics are encouraging students to give up their focus on mundane jobs and try something they would enjoy themselves. 

Industries are beginning to admit and accept the value of data analysts and thus throwing open positions that can be great catches for students who have acquired skills in data analytics. Every industry is constructively using its data, and thus the landscape is growing more vast with each passing day. 

Develops Problem Solving Skills 

Problem-solving skills are not just important for the job of data analytics but can also help in several other fields and even in your personal life. And analytics is particularly about problem-solving. Developing the skill to think and analyze is an important one, and data analytics helps you do just the same. 

Increasing Importance 

The analytics boom is taking over the world, and it is the time when its importance is at its peak. So we can expect newer fields to crop up pertaining to data analytics. If you learn data analytics well enough, you are simply bracing yourself for a future that will probably be highly reliant on analytics. 

How to Find a Job Interview Guarantee Program 

There are several data analytics programs in the market. Some of them are online, while others are offline courses. But getting a good course is not enough.

Follow these tips to land a course 

  • Look for a course that offers a good program and covers all the major important areas. 
  • The course should not be less than three months as it is the minimum time required to learn the basics of data analytics. 
  •  Look for industry experts and IIT faculties on the team. 
  • Lastly, make sure the course is tied up with big companies as it increases its credibility substantially. 

Conclusion 

Data analytics is the future of every industry and might emerge as the biggest industry shortly. Taking a course in this field will not only help you get job opportunities but will broaden your horizon of data, which can be extremely helpful to your career. 

Top 10 Popular Data Analytics Tools For A Better Career

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Top 10 Popular Data Analytics Tools For A Better Career

Data is definitely today’s one of the most valuable assets. We all know the limitless power of data and how it dictates business operations. Whatever the scale of a business, data makes the core of it. Not only businesses or IT hubs are using data analytics, but various other fields are also taking the advantage of it. Let us take a very simple instance to understand how data analysis works.

Let us take sports as a case study. This will help you understand that a field that is remotely connected to science and technology is extracting its benefits. Assume X is a tennis player who has been on the circuit since 2005. He has been touring all over the world, participating in various tournaments, and has recently put his shoes off. Now, you get to see his performance graphs, how consistent he was, which were his favorite shots, etcetera on television channels. This is the most common scenario when the stats are analyzed post-retirement. Have you ever thought about how that is possible? 

 For instance, all the records and stats right from his first match to the final match with a comparative analysis require rigorous data analytics. It is humanly impossible to gather all the data and then filter out necessary ones manually. 

Here comes data analytics tools that help you perform the task effectively. Go through the upcoming sections to get familiar with the new-age data analytics tools.  

An Introduction to Data Analytics

We are currently living in the world of data and therefore you will often be advised to learn data analytics. 

Data analytics tools are particular applications and/ or software that enable data analysts to work with a large volume of data. Data, in its crudest form, is unstructured and haphazard. A data analyst filters out unnecessary data and then collates the required information in the database. This is structured data that can be used effectively for future references. 

Data analytics tools help manage and work with data. In other words, useful data is actually the derivation of structured data via data analytics tools. Find below a list of the latest tools that are used by the giants of the industry like Google, Facebook, Apple, Dominos, and others.  

Top 10 Data Analytics Tools

Python

One of the most widely used programming languages among AI developers today is Python. It can be used effectively to run programs because it does not need to be pre-compiled into machine code. Additionally, it has an automatic memory management system and a dynamic type system that support a wide range of programming paradigms.

Microsoft Excel

It may sound trivial but in reality, Microsoft Excel works as a strong data analytics tool. Numerous filters, formulae, and other features enable users to analyze data effectively with Microsoft Excel. Asa matter of fact, this is the very tool we all perhaps get to learn in our schooldays. 

RapidMiner

As the name suggests, RapidMiner is an excellent data analytics tool used for data mining. Data mining includes scopes such as visualizing, filtering, structuring, storing, and managing big data. 

QlikView

QlikView is a self-service business analysis tool that you should learn to be at par with the most recent trends in data analytics. Samsung and CISCO are among the tycoons that use QlikView for data analytics.

Apache Spark

Apache Software Foundation came up with one of the most useful data analytics tools that empower users with multiple features. Above all, this is an open-source application used by Visa, Oracle, and other big names. 

Talend

If you are eyeing a data integration tool, then Talend is the suitable solution for you. Talend incorporates an ELT backbone which ensures the safety and simplicity of the interface. 

Splunk

Splunk is among the widely used data analytics tools to visualize, filter, and analyze raw data. It is favored and trusted by 92 of the top Fortune 100 companies.  

Tableau

Tableau offers you a simple, user-friendly interface to work with a cluster of structured and unstructured data. It is one of the rare tools that allows you to work on live data sets. 

Datapine

If you want to get a grasp of data analytics, you should start with a simple yet effective analytics tool. Datapine is one of those tools that allows beginners to work with a large scale of data without complicating things.  

SAS Forecasting

Data forecasting or data prediction is an important part of data analytics. For example, you may need to predict a stat for the future based on available current data. SAS Forecasting is the ideal tool to do such things easily.  

Best Data Analytics Certification Course

Data analytics is a topic that has earned a reputation in and outside India as one of the vital operational wings of the business. Competent data analysts are placed in big national and international agencies with handsome salaries. Thus, students often look for the best data analytics certification course. Imarticus has an integrated post-graduation course on data analytics that prepares trainees for a bright future. 

Data Analytics Course with Placement

You should learn data analytics from a reputed institution that offers you a data analytics course with placement opportunities. 

Placement immediately after the course will boost your morale and also introduce you to the real world of data analytics. You will get to know how the giants of industries run their businesses with the power of effective data analytics.  

Machine Learning A-Z: Hands-on Python and R in Data science

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R and Python are both equally great programming languages. However, each has its own set of advantages that it offers to the user. For example, when we are talking about development processes or IT operations, Python is always a better option. But when it comes to statistical tasks or analytics, R can prove to be a much more suitable alternative.

This is because R has been created for Statisticians and for statistical projects. Skilled programmers can, however, employ any of the two languages to perform any task. For instance, Python can be used for using statistical techniques like Regression Analysis or Bayesian inference on datasets. Similarly, R can also be used for building new Data Science tools and data models. 

Python is quite easy to learn with simple syntax while R has various language boundaries. One can easily learn Python online with the help of a course. However, R is far superior in graphical and statistical procedures. R is more scientific in nature and will be much easier for statisticians or professionals who have worked with MATLAB, another scientific programming language.

Python is more focused on running algorithms and makes it easy to build programs in general. In Data Science, however, both are equally important and competent languages. R and Python can work with massive databases and are equally good at Machine Learning projects. 

R in Data Science

R is used in Data Science for graphical and statistical purposes. This language can help users create advanced visualizations and high-quality graphics as well as dynamic graphics. R is also great for data mining and statistical computing in general.

R has a set of functions that allows datasets to be loaded into the memory with program statements, but that is only required if you are going to be building R programs that will keep getting used for various functions. Otherwise, you can simply use the data import function in R Studio (IDE for R). R is able to work with data in two different formats in R studio, CSV and TXT. Once you select the dataset you wish to be working with, R will load the dataset and you can then work on it.

Python in Data Science

Python is a high-level programming level that does not require compilation before running code. This makes it very fast to use for all kinds of projects, including Data Science and Data Analytics. Python is extremely flexible and is a multi-paradigm language, thus, allowing Data Scientists to use different approaches and keep improvising the language with extensions and plugins.

Before loading a dataset in Python, you must first import pandas (library) and install them in Python. After that, a custom function must be created that loads the dataset for you. By creating a load_csv function, you can fundamentally provide an argument for your dataset’s file path.

Also, the readlines() are used in order to ensure that the program returns a list containing the lines inside the .csv dataset. You can also additionally present the data in a more visually appealing way by returning the dataset in a data frame format. This makes it easier to view the data as compared to the native list format or NumPy arrays.

Conclusion

Both the languages also are open-source and have huge communities behind them to keep providing new libraries. There are also a good number of updated tools and powerful IDEs (Integrated Development Environments) available for both R and Python.

If you wish to learn Python or R for Data Science, you should definitely opt for a solid Post Graduate Program in Data Science. You can also choose to take up a Machine Learning course with a placement or a Data Analytics course with placement as well. 

A quick and easy guide to Tableau

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A quick and easy guide to Tableau

Tableau is a famous data visualization software that helps people see and understand data. If you’re looking for an easy way to get started with Tableau, this guide will walk you through how it works to what types of visualizations are available in this tool.

What is Tableau?

Corporate leadership

It is a data visualization tool that makes it easy to view and analyze your data. It helps them visualize their findings so they can see patterns quickly and easily—and then act on those trends before they become problems for the company.

Why should you learn Tableau?

A tableau is a powerful tool that both individuals and organizations can use. It’s easy to learn and use, but its true power lies in the data visualization it allows you to create. 

A tableau is a powerful tool that’s easy to learn, use and collaborate with. You can use it in many ways:

  • To make visualizations (charts, graphs)
  • To create dashboards and reports
  • To work with others on data analysis projects

Types of visualizations in Tableau

There are many types of visualizations that you can create in Tableau. Here’s a quick overview:

  • Bar charts. Bar charts show the distribution of values in your dataset (e.g., sales by month). You can use bar charts to compare deals over time or across groups, such as how many people purchased products this year vs. last year or what percent each type of product accounted for at different times during the day (or week).
  • Line charts. Line graphs show trend lines on a chart based on some measures and other data points like age and gender. However, we still want some idea about whether any change is happening over time because otherwise, it would make no sense why one person might be doing better than another even though both have been using the app since the launch date!

Tableau course helps people analyze their data by allowing them to visualize their information visually in a way that makes sense. For example, they might want to see how many products were purchased by customers at different times or the average price for each product sold during the month of last year. Tableau allows you to accomplish these things quickly to make informed decisions based on what your data tells you about your business or project.

Discover a career in data science with Imarticus Learning

Become a data analyst with this data science course that guarantees job assurance. Learn data science practically and create complex models that generate critical business forecasts and insights.

Course Benefits for Students:

  • Students will be familiar with data analytics, machine learning basics, and the most extensively used data science tools and methodologies.
  • To get a data analytics certification course, students must complete 25 real-world projects and case studies led by business partners.
  • The ability to display data using a data analytics tool is one of the most sought-after skills in the industry today. As a result, new graduates and those just beginning their jobs may want to consider enrolling.

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