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Tag: data analytics career

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.

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

Posted on by Imarticus Learning

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 Machine Learning is the Key to Unlocking Insights from Big Data?

Posted on by Imarticus Learning

Big data is becoming increasingly important in today’s business landscape. But what is big data, and why is machine learning the key to unlocking insights?

Data Analytics Course

In today’s digital world, we generate massive amounts of data every day.

From our social media interactions to online transactions, the data keeps growing.

However, the real challenge lies in understanding this data. Uncovering valuable insights from data can help us make better decisions.

This is where learning machine learning becomes essential. It is a key tool for extracting meaningful insights from large data sets.

What Is Machine Learning?

Machine learning is a part of artificial intelligence that focuses on developing algorithms and models.

It enables computers to learn from data. Instead of following strict rules, machine learning algorithms learn from patterns and trends in the data. Thus allowing them to make predictions, identify patterns, and reveal insights.

Importance Of Machine Learning In Unlocking Big Data Insights

Learning machine learning offers several advantages when it comes to analyzing large data sets. Traditional methods of data analysis struggle to handle big data due to its enormous volume,.

Humans would find it challenging to manually go through such vast amounts of data to extract insights. Machine learning algorithms, on the other hand, excel at processing and analyzing extensive data sets. It helps in revealing valuable insights that might otherwise remain hidden.

One of the main strengths of learning machine learning is its ability to identify complex patterns within the data.

These patterns can be challenging for human analysts to detect, especially when dealing with multi-dimensional data. Machine learning algorithms can identify and analyze intricate connections between variables.

Thus enabling organizations to uncover valuable insights and make data-driven decisions.

Furthermore, learning machine learning algorithms can continuously learn and adapt to new data. It improves their performance over time. This iterative learning process allows models to become more accurate and reliable. It helps, making them ideal for handling dynamic and evolving data sets. By learning from past experiences, machine learning models can make predictions and recommendations based on historical data. It helps businesses in anticipating future trends and making proactive decisions.

Imarticus Learning’s Postgraduate Program in Data Science and Analytics

Imarticus Learning recognizes the growing importance of machine learning in the field of data science and analytics.

The Postgraduate Program in Data Science and Analytics is a 6-month online course. This course offers numerous benefits to its participants:

Live Learning Modules

The program provides live learning modules. It allows students to interact with instructors. This interactive approach enhances the learning process and encourages active participation.

Dedicated Career Services

Imarticus Learning offers dedicated career services to support students in their job search. They provide guidance and assistance to help students find suitable employment opportunities in this industry.

Access to KPMG Organized Hackathons

Students enrolled in the data analytics course gain access to hackathons organized by KPMG. These hackathons provide an opportunity to apply machine learning techniques in a competitive setting.

Real-World Projects for Practical Knowledge

The program incorporates various real-world projects. It allows students to apply machine learning concepts to solve practical problems. This hands-on experience bridges the gap between theoretical knowledge and its practical application. It helps prepare students for real-world challenges.

Job-specific Curriculum

The program is designed to provide students with the specific skills required to excel in the field of data science. By focusing on the practical aspects of the industry, the curriculum ensures that students are well-prepared for the job.

Job Interview Guarantee

Imarticus Learning provides a job interview guarantee to eligible candidates upon successful completion. This assurance gives students confidence in their career prospects. It serves as an incentive to excel in their studies.

Alumni Success

Imarticus Learning’s alumni are currently employed by various esteemed companies. Some top companies are Accenture, KPMG, Infosys, Capgemini, IBM, Cognizant, Wipro, Standard Chartered, and Deloitte. The program’s strong industry connections provide graduates with excellent networking opportunities. This increases their chances of securing rewarding positions in top organizations.

Final Thoughts

Machine learning plays a vital role in extracting valuable insights from large data sets. Its ability to analyze large amounts of data, and identify complex patterns, makes it important in today’s data-driven world.

Imarticus Learning’s Postgraduate Program in Data Science and Analytics offers a comprehensive curriculum that covers all aspects of data science and analytics.

Join Imarticus Learning’s Postgraduate Program in Data Science and Analytics. Start your journey towards a rewarding career.

The Impact of Data Science on Various Industries

Posted on by Imarticus Learning

Data Science and Industrial Integration

Data science studies the extraction and derivation of meaningful insights from data. The approach to gathering the insight is multidisciplinary.

It takes practices and principles from a variety of fields. Some of these fields are computer engineering, statistics, artificial intelligence, and mathematics.

The applications of data science are unlimited as it answers the four important questions of problem-solving. These are what is the situation, why is it, what is the future course, and what would the result yield.

best big data analytics course

This basic information gathering and problem-solving make the adoption of data science in different industries feasible.

Data science finds its application in industries such as retail, medicine, banking, finance, and telecommunications among others. Read ahead if you are planning to make a career in data science and are curious about the impact on various industries.

Impact of Data Science on various industries

A career in data science requires professionals to make use of raw data for the identification of patterns. These patterns are then used to derive actionable insights from the data.

With data science, it is possible to predict future outcomes with a higher level of accuracy. Here is how various industries are benefiting from the applications of data science.

1. Healthcare

Applications of data science in the healthcare industry include the use of data in taking important decisions and drawing conclusions. These include the implementation of medical knowledge gathered in the diagnosis of diseases.

Data science is also used to improve the safety and quality of healthcare by helping design prevention plans. It has also helped chart important health parameters such as sugar level, stress level, brain activity, etc using trackers. This can help deliver personalised care and precise prescriptions.  A deep learning technique is used in reading imaging data. This is also to reduce diagnostic failures.

2. Retail

In the retail industry applications of data science have led to the implementation of features such as recommendation engines. The impact helps companies design products according to the needs of the consumer. Market-based analysis allows the company to determine the likes, dislikes, and price points of consumers. This allows companies to target the right audience more efficiently.

The science has also helped the industry to analyze customer sentiments more accurately. Social media and other points of contact between consumers and companies help companies collect data. This is further used to enhance the feedback mechanism. The feedback is then used to design better products. Firms using data science can optimise prices efficiently. All these lead to an increase in sales and revenue flow in the industry.

3. Banking, Finance Services, and Insurance Industry (BFSI)

BFSI has benefitted largely from the application of data science in the industry. The industry has been able to minimize its losses with the help of fraud detection using science. It has impacted more efficient management of customer data, risk modelling, and customer support.

Data scientists can generate customer life value prediction that is used to recommend products and services to consumers as per their needs. BFSI companies are also able to minimize the impact of any risk to their businesses with the help of predictive and real-time analysis. These techniques have led firms to increase sales and operational gains.

4. Telecommunication Industry

If you are looking for a career in data science in the telecommunication industry you will be able to impact efficient data transmission and visualisation needs. Data science is applied for increasing fraud detection by firms. Using science, consumers benefit from increased network security.

The benefits for the company include price optimisation, real-time analysis, and prevention of customer churn. Companies can also benefit from features such as location-based promotion, predictive analysis, and targeted marketing.

5. The course you need for a career in data science

The example of the impact of data science on four industries is a mirror of the significance of data science in today’s world. If you are looking for a career in data science Imarticus Learning’s Postgraduate Program In Data Science And Analytics is the one.

The course is designed for early career professionals. This six-month program is designed based on a job-specific curriculum. The learning is accentuated by live learning modules, real-world projects, and KPMG hackathons. The certification is designed to launch your career in data science in an industry of your choice.

Summing it up

Applications of data science are wide. There are several industries apart from healthcare, retail, BFSI, and telecom that are using data science. Data science can help companies grow and the skills of a data scientist are always in demand. You can benefit from a quick start in your career by being a part of Learning’s Postgraduate Program In Data Science And Analytics.

Imaritcus Learning offers you benefits such as Job-assurance, specialized training, and placement drive. It has helped more than 56000 individuals attain placements. Be sure to visit us for other similar courses.

How learning data analysis can impact your career?

Posted on by Imarticus Learning

Data analysis holds the key to the future of most businesses. Earlier, the resultant data of any transaction was either not captured at all or, at best, recorded manually. Such details would be usually generated and preserved in excel sheets and used by an organisation to make data-driven decisions. However, such manual practices are unimaginable now. The main reason is the massive volume of data, often referred to in this domain as big data. A career in data analytics, thus, is in huge demand. 

Nowadays, every organisation is digitally evolving. Business houses are using digital platforms to gather substantial feedback from their clients. This model may be best understood when someone chooses a product or service from an E-commerce platform. Past client reviews and earlier product ratings often guide the choice. The manufacturer or the dealer also gets feedback on the platform from the client and understands the product’s sustainability and demand in the market.    

Role of A Data Analyst 

data analytics course

The function of a Data Analyst begins with recognising and gathering raw data. The next roles include cleaning or filtering the available raw data. The data is then worked upon using tools like advanced excel, statistics, and programming languages such as Python & R, etc., and presented in the form of easily understandable visuals like bar or pie charts. Thus, the knowledge of Power BI and Tableau is equally important. All these tasks of a Data Analyst are directed towards assisting or supporting the management of a business house to make a path-breaking and futuristic decision. This would pave the way for a targeted top line in the business with desired profit at the end of the financial year. Besides these, data analysis also helps the company to locate the present, and future potential of a said business, irrespective of whether it is a product or a service. The process can also predict whether a product should be discontinued from the market or a new product should be launched soon. 

How to Become A Successful Data Analyst?

Data analysis may be taken up as a career by individuals who don’t have a Bachelor’s degree in Software Engineering. However, it should be remembered that aspiring candidates who want to learn data analysis must have a strong inclination towards the subject with an aptitude to learn and grasp complex topics in the subject domain. Since this job involves handling different algorithms and programming types, knowledge of languages like C++, Java, R, and Python becomes essential. Candidates don’t need to learn all the languages simultaneously. However, starting with R & Python might prove to be helpful. Over time, the candidate is free to master other useful languages to secure a better understanding of the domain. 

Apart from the hard-core technical knowledge of the profession, knowing about the services offered by the organisation or the product manufactured is desirable. The more the candidate develops this knowledge, the better the aspirant may analyse the data. The accuracy of prediction or extrapolation of data will also have far-reaching effects. 

A successful Data Analyst should have both an innovative and inquisitive mind. He should develop a habit of taking notes from different research papers on the subject and from videos.

Along with all these, candidates aspiring to become Data Analysts or existing data analysts must create a community of people with similar educational backgrounds and common professional interests. Within the circle, they can discuss good practices and try to absorb them in their daily activities. Listening to success stories from professionals from a different background other than software engineering will also boost the morale of aspiring job seekers in this domain.

Conclusion

The job market for a Data Analyst is debatably growing at the fastest rate as per industry standards and shall have a very high demand in days to come. Every organisation is now relying on data as its prime mover. Whether it is an overall prediction for a business market or a key decision regarding an upcoming or an existing product or service, data analysis is the only modern solution in today’s world. With an advanced analytical mind and a strong command over the business domain, young Data Analysts may aspire to become Data Scientists someday. 

A prospective candidate can learn data analytics from the postgraduate program in data science and analytics course taught at Imarticus. It is one of the best data analytics courses in India, taught over a span of 6 months. This course will help you to achieve your dream. Head to the website to learn more about the course.

Introduction To Data Analytics: Technologies, Jobs and Careers

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Introduction To Data Analytics: Technologies, Jobs and Careers

Nowadays, Data Analytics has become a sort of a buzzword in the data science industry. Just have a look at any of the job websites, and you will see that there are many vacancies in data science. Even Harvard University has labelled it as one of the hottest job fields in the USA, and India is also a part of the same trend.

So, what is the reason behind this craze for data analytics and data science jobs? Let’s have a look.   

What is Data Analytics, and what does a data analyst do?

Data Analytics is a subset of Data science. It is where raw data is cleaned, analysed, interpreted and displayed using several approaches with the help of business intelligence tools. These data analysis tools transform raw data into interpretable information that aids in making successful decisions.

But how is the data presented? Raw data is converted into meaningful information, statistics and explanations with the help of tools and techniques. There are multiple tools that data analysts use for generating all the interpretations of data.

At the same time, there is some programming expertise that a data analyst requires. For example, they could learn either of the two programming languages, R and Python, to process the data. For storing this data, they could use SQL. But the entire tech tools a data analyst needs are mentioned in the next section. So, just continue to read the topic till the end.

Technologies employed by Data Analysts

Several tech tools and programming languages can make the life of a data analyst easier. As already mentioned, data analysts have to process raw data and use it to generate various graphical representations. For that, they need to –

  • Identify the data they require
  • Collect raw data from the specific source they have identified
  • Clean the data and remove the inconsistencies
  • Analyse the data
  • Interpret the results

To perform these functions, data analysts use several tools. First of all, let us delve deeper into the programming languages that come in handy.

  • Python
  • R
  • SQL
  • Hadoop for big data

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The best thing about these programming languages is that there are multiple online resources to master these programming languages. You will find millions of tutorials to learn Python online.

Apart from the programming languages, you need to master some of the tools that are mentioned below:

  • Google Sheets
  • Microsoft Excel
  • Tableau
  • SAS(Statistical Analytical System)
  • Microsoft Power BI
  • Jupyter Notebooks

Mastering these tools and programming languages is quite important because they help you by making your job easier. You get to learn all these in a good data analytics certification course, and you can get in touch with us at Imarticus Learning.

Jobs for Data Scientists

Data science jobs stretch across various sectors. It has always been used for business, in both sales and marketing. Not only that, it is also handy for retail so that the inventory can be managed. Apart from business, it is used in medical infrastructure, operation research, finance and allied sectors. It is also being used in the entertainment field for various data-based jobs, especially for calculating the TRP of various shows.

Now it is also being used in education, law, security, automobile industry, energy sector supply chain, social media and in various fields of research. Data analysis has crept into almost every industry as it is finding a lot of applications in various fields.

But which are the most popular roles that are also enjoying a huge career and salary growth? Some of these roles are mentioned below:

  • Medical and health care analyst
  • Market research analyst
  • Business analyst
  • Business intelligence analyst
  • Operations research analyst
  • Intelligence analyst

These jobs are the top roles across various industries and job sectors. You will find multiple listings of these roles in all the leading job portals. Some of the beginner roles start above the ₹5,00,000 pay bracket. However, if you have completed a good data analytics certification course, then you will be able to command a good salary from your employing organisation.

The overall career of a Data Analyst

Choosing data analysis as a career path is a great decision, especially in the current market. After bagging a beginner role, try to gain more technical skills. Then you move to the position of a data scientist.

But there is a clear bifurcation of the career paths. For example, if your interest largely lies in statistics and data analysis, then try out business intelligence and analysis. But if you love the tech side, pick data science and data engineering as your career.

To conclude, the opportunities for data science jobs in the current scenario are increasing every year and are slated to grow even further in the coming years. So, being prepared with the necessary knowledge and technical know-how will greatly boost your career. In this regard, you can always look at the Post Graduate Program in Data Analytics and Machine Learning from Imarticus Learning to enhance your skill set. 

Top 10 Popular Data Analytics Tools For A Better Career

Posted on by Imarticus Learning

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

Posted on by Imarticus Learning

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

Posted on by Imarticus Learning

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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