Contact Us
×

Tag: Predictive analytics course

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.

Why Learning Python Is Essential For Data Science

Posted on by Imarticus Learning

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!

Why Data Analytics Courses with job-interview Matter in Today’s Competitive Job Market?

Posted on by Imarticus Learning

In today’s fast-paced and data-driven world, organizations increasingly rely on data analytics to make informed decisions and gain a competitive edge. As a result, the demand for skilled data analytics professionals has skyrocketed, creating numerous job opportunities in various industries. However, breaking into the field of data analytics can only be challenging with the proper training and practical experience. 

Rapidly Growing Importance of Data Analytics

best big data analytics course

Data analytics involves extracting insights and patterns from large volumes of data to drive strategic decision-making. It enables businesses to understand customer behaviour, optimize processes, identify trends, and uncover hidden opportunities. With the exponential growth of data in recent years, the need for professionals with data analytics skills has become indispensable across industries such as finance, marketing, healthcare, retail, and more. Employers are actively seeking individuals who can leverage data to improve business outcomes and drive innovation.

Benefits of Data Analytics Courses with Interview Guarantee

The Significance of Data Science Courses with Job-interview Guarantee in Today’s Cutthroat Job Market

  • Acquisition of In-Demand Skills: Data analytics courses provide students with a comprehensive understanding of key concepts, tools, and techniques used in the field. These courses cover various topics, including data mining, statistical analysis, predictive modelling, data visualization, and machine learning. By acquiring these in-demand skills, individuals become highly sought after by employers.
  • Practical Training and Hands-on Experience: One of the significant advantages of data analytics course is the emphasis on practical training. Students can work on real-world projects, analyze datasets, and apply their knowledge in a simulated work environment. This hands-on experience prepares them to tackle real-world challenges and enhances their problem-solving abilities.
  • Guidance and Support in Job-interview Guarantee: Data analytics for beginners often have dedicated programs and industry tie-ups. Students receive guidance and support from experienced professionals who help them navigate the job market. These programs assist in resume building, interview preparation, and connecting students with potential employers. By leveraging the network and expertise of industry experts, students have a higher chance of securing job interview opportunities.

The Role of  Interview Guarantee Programs in Career Success

Data analytics courses play a vital role in bridging the gap between education and industry requirements. They provide a platform for students to showcase their skills and connect with employers actively seeking data analytics talent. These programs offer valuable networking opportunities and often organize recruitment drives, job fairs, and industry-specific events. Students participating in programs gain exposure to potential employers, leading to internships, entry-level positions, and long-term career opportunities.

Exploring the Relevance of Data Science Courses with Job-interview in the Competitive Employment Landscape

Several factors should be considered when choosing a data analytics course with interview guarantee to ensure a well-rounded and effective learning experience.

  • Curriculum Tailored to Industry Needs: Look for courses that offer a comprehensive curriculum aligned with the latest industry trends and requirements. The course should cover both theoretical concepts and practical applications, providing a holistic understanding of data analytics.
  • Quality of Faculty and Industry Experts: The expertise and experience of the faculty members and industry experts involved in the course are crucial. Qualified instructors with real-world experience can provide valuable insights and mentorship throughout the learning journey.
  • Internship Opportunities and Industry Collaborations: Check if the system offers opportunities for internships or collaborations with industry partners. Internships provide valuable hands-on experience and the chance to work on real projects under professional guidance.

How Data Analytics Courses with Job-interview Boost Career Prospects

Data analytics skills are highly sought after in various industries, offering a wide range of career opportunities. By enrolling in a data analytics course , individuals can significantly enhance their career prospects in the following ways:

  • High Demand Job Roles: Data analysts, data scientists, business analysts, and data engineers are some of the job roles in high demand. These roles come with competitive salaries, growth opportunities, and the chance to work with cutting-edge technologies.
  • Lucrative Salary Potential: Data analytics professionals command competitive salaries due to the scarcity of skilled individuals in the field. With the right skills and experience, professionals can earn a significant income and enjoy financial stability.
  • Career Growth Opportunities: Data analytics is a rapidly evolving field, providing ample opportunities for career growth. Professionals can progress to managerial or leadership roles, specializing in niche domains, or explore entrepreneurship by leveraging their data analytics expertise.

In today’s competitive job market, data analytics courses offer a strategic pathway to career success. These courses provide individuals with the necessary skills, practical experience, and job interview support to excel in data analytics. Individuals can position themselves as precious assets in the data-driven economy by investing in such courses.

Final Words:

The importance of data analytics courses cannot be emphasized in today’s extremely competitive employment environment. The need for knowledgeable data analysts has increased as businesses rely on data-driven insights to make wise decisions. Enrolling in a data analytics course will allow you will acquire the skills you need to succeed in this fast-paced industry and land satisfying career possibilities.

Imarticus Learning offers a comprehensive data science course and a unique opportunity for recent graduates and professionals to gain practical knowledge and skills in this rapidly growing field. 

Join us and embark on a successful data science and analytics career where you can make a tangible impact on real-world businesses.

Data Cleaning and Preprocessing: Ensuring Data Quality

Posted on by Imarticus Learning

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.

data analytics course

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.

The two paths from Natural Language Processing to Deep Learning

Posted on by Imarticus Learning

The two paths from Natural Language Processing to Deep Learning

Natural Language Processing is a branch of linguistics, computer science, and artificial intelligence that deals with the interaction between computers and human language, in particular how to design computers to handle and evaluate huge volumes of natural language data. We want a computer that can “understand” the text in documents, including its context and subtleties.

As a result, the papers’ data and insights may be correctly extracted by the technology, which can also classify and arrange the documents themselves.

Massive amounts of unprocessed, text-heavy data need a system like this, which is widely used in machine learning. Professionals with expertise in designing models that analyze voice and language, find contextual correlations, and generate insights from this unprocessed data will be in high demand as AI continues to grow. Natural Language Processing and Deep Learning with Python are one of the most common phrases used in the domain of Artificial Intelligence nowadays.  

In machine learning and artificial intelligence, a technique known as “deep learning” mimics human learning processes. Data science, which encompasses the statistical analysis and forecasting models, relies heavily on deep learning techniques to do its work. For data scientists, deep learning is a godsend since it speeds up the process of processing and understanding massive volumes of data. 

It is possible to think of deep learning as the automation of predictive analytics. Deep learning algorithms are piled in a hierarchical structure of increasing complexity and abstraction, while typical machine learning algorithms are linear.

Neural Networks and Deep Learning

A Neural Network, also known as an Artificial Neural Network, is made up of layers. Imagine the neurons in a human brain; they are the computing units, and they form a single layer when layered. stacking neurons together creates several layers. It is termed the input layer because it contains the data that we are working with. We run our algorithms and get an output, which is then utilized to do our computations for the following layer, the output layer.

At each successive layer, all of one layer’s neurons are linked to those at each successive layer, which are then linked to the next layer, and so on, until we reach our output layer, where we achieve our desired outcome for the specific data we were working with. Those layers that are between the input and output layers are referred to as Hidden Layers. A Neural Network is the result of this process.

A deep neural network is an artificial neural network with two or more hidden layers, and a model built on a deep neural network is referred to as Deep Learning. 

What are the main components of Natural Language Processing?

NLP consists of a number of components, a few of them are mentioned below:

  • Analysis of morphological and lexical patterns.
  • Syntactic Analysis: Study of logical meaning from a given part of the information, be it text or audio.
  • Semantic Analysis: Used to analyze the meaning of words.

Keen To Develop A Solution That Will Predict User’s Online Training? Imarticus Could Help You Develop The Right Expertise

Posted on by Imarticus Learning

Online user behavior is something e-commerce and target marketing has been relying on. What helps them predict online user behavior is machine learning and Artificial Intelligence.

The user interest and the pertaining trend help the business and marketing team to anticipate the changes and take the necessary steps for improvement. This change is a trendsetter that has urged many other fields to use the same mechanisms to study its users.

data analytics and artificial intelligence courses in IndiaEver since the online training process gained popularity finding out what the users’ needs became crucial to set up the right kind of courses and setting up the ideal curriculum to nourish their careers.

Predicting user’s online training

Getting the correct feedback on the online learning process is significant to know about what the users are interested in. It helps refine the curriculum and also helps introduce more useful courses to increase the chances of a better career.

Predicting a user’s online training interest requires some specific expertise but it doesn’t involve a single field of study. It requires a collection of subjects that are part of data science. In order to be an expert in predicting user interests, these are the fields of technology that one must master.

  • Artificial Intelligence: User prediction using AI is done through developing models and algorithms that provide a predictive outcome. It is also one of the primary objectives of this technology.
  • Machine Learning: ML is the branch of AI that helps with the computational part or where the machines are made intelligent to work like human brains. BY processing the machines one can help find the relevant data to create a reliable predictive analysis to find out the user interest.
  • Deep Learning is that part of AI that functions as human brains where it recognizes speech, language, objects, etc and that also helps in decision making. Using these factors creates a prediction model that has near-accurate validity. In fact, it is one of the most popular careers at the moment.
  • Natural Language Processor NLP is using technology to understand the common and natural language used by humans. It involves compiling data and processing it appropriately through mathematical, linguistic, or statistical methods. It is just a collective term for multiple steps. Moreover, it uses most of the above mentioned technologies as well.
  • Computer Vision is another branch that works on perspective analysis using videos and images. They help with replicating the visual system in humans and automate the tasks accordingly.

How Imarticus helps?

Imarticus offers various courses in Artificial Intelligence that includes Deep Learning, Machine Learning, Natural Language Process, Computer Vision, etc in the curriculum. It makes a foundation for predictive analytics. The course duration ranges from 2-8 months depending upon the user’s choice. Both online and classroom options are available for most courses.

Analytics and Artificial Intelligence coursesAnalytics and Artificial Intelligence Courses in India are open to both freshers and working professionals. They can select the relevant option when opting for the course. The courses are termed PG-courses and Pro-Degree courses respectively.

Keeping in mind the time constraints for the professionals, the pro degree courses may be completed in a shorter span of time.

Not only do they provide quality courses but also provide opportunities to work with giants such as IBM and others. Both types of courses have this partnership option for hands-on experience.

Analytics and Artificial Intelligence courses in IndiaSuch hands-on courses enable the users to polish their knowledge and make them borderline experts in their respective fields, even for an entry-level.

Conclusion

The learning process at Imarticus includes a program mentor who gives proper guidance and support for the academics. Moreover, the placement assistance at the end of the course increases the prospects of landing the right job.