Technology is playing a bigger role in every sector of businesses to collect relevant data, conduct meaningful operations, etc. They monitor client data, corporate performance, and sales trends. In this era of technology and the digital revolution, learners can review this highly demanding course and make a successful career in the thriving job market of data analytics. In this read- IIM SKILLS vs. IIT Madras (IITM Pravartak) Data Analytics Course, we will include two educational institutions and the required details about data analytics.

IIM SKILLS vs. IIT Madras Data Analytics Course Review

Data Analysis

After gathering data using empirical methods, the following stage is data analysis and interpretation. The distinction between data analysis and interpretation is difficult to make since the two processes are symbolic and blend invisibly. Analysis and interpretation are intricately linked.

The analysis involves a thorough study of the gathered data. Data analysis leads to generalization.

The analysis of generalizations and findings is referred to as interpretation. A generalization is a conclusion about an entire group or category based on knowledge collected from specific cases or examples.

A deep search for the significant findings is known as interpretation. Data analysis will be performed about the study’s purpose.

Data should be examined and structured in light of hypotheses or research questions to produce responses to the research questions.

Data analysis can be presented in both descriptive and graphic form. It can be presented in the form of charts, graphs, or tables.

Data analysis contains several activities, including data classification, coding, tabulation, statistical data analysis, and conclusions related to other variables.

Proper and effective analysis aids in classifying and organizing disorganized material, as well as providing scientific shape. Furthermore, it aids in the analysis of trends and changes that occur throughout a specific period.

Methods of Data Interpretation

Analysts use data interpretation methods to assist individuals in making meaning of numerical data that has been collected, evaluated, and presented. A raw form of data might be difficult for the analysts to grasp; therefore, analysts disintegrate the information so that the data processing can become hassle-free. There are two basic ways to accomplish this: quantitative approaches and qualitative ones.

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Method for Interpreting Qualitative Data

The qualitative data interpretation method is useful in surveying categorical and undiluted data, also known as qualitative data. This approach uses text in data rather than numerical or models. Proportionately the quantitative mechanism of research, and qualitative data are consistently collected through a wide range of person-to-person survey techniques that might be challenging to interpret. In contrast to quantitative data, which can be examined thoroughly and immediately after being collected and sorted, qualitative data must first be coded into numbers before it can be analyzed. This is because analyzing texts in their original state takes more time and results in many inaccuracies. 

The analyst’s coding should also be documented so that it can be reused and evaluated by others. There are two sorts of qualitative data: nominal data and ordinal data. Both of these data kinds are interpreted using the same procedure, although ordinal data interpretation is much easier than nominal data interpretation. Ordinal data is typically labeled with numbers throughout the data-collecting process, and coding is not always required. This is distinct from nominal data, which must still be tagged for correct interpretation.

Method for Interpreting Quantitative Data

The quantitative data interpretation method is used to assess numerical data, also known as quantitative data. This data type comprises numbers and is hence processed using numbers rather than textual. There are two forms of quantitative data: discrete data and continuous data. Continuous data is further subdivided into interval data and ratio data, all of which are numeric. Analysts do not need to use the coding procedure on quantitative data before analyzing it because of its natural existence as a number. Analyzing quantitative data requires the use of statistical modeling techniques such as standard deviation, mean, and median.

Data Analytics Requirement in Businesses

It is deemed a fact that data analytics techniques are beneficial for businesses’ flourishing of all parameters. It assists marketers in designing and accommodating seamless customer journeys and modifying content accordingly to fit the fashion.

According to sources, despite access to consumer data, most marketing professionals claim trouble making data-driven decisions ranging from forecasting customer purchases.  

The difficulty is having access to the technical capability of evaluating large amounts of data instantly. While many businesses are collecting vast amounts of data, it has become difficult for them to handle extensive data efficiently and proficiently.

Businesses in all industries can benefit from data analysis, which allows them to stand out from the competition. Such techniques may also detect potential problems or prevent fraud, notably in the pharmaceutical or financial sectors.

The goal for e-commerce enterprises is to exploit data to their advantage. These businesses gain a better understanding of their customers’ wants and habits by analyzing their users’ browsing behavior. 

This information then assists firms in increasing their profitability. It also allows the corporation to promote things that specific consumers are more likely to order and purchase online.

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Understanding About Data

A corporation or any organization tackles vast volumes of data, and it is a heavy responsibility to handle and develop a long-term strategy and purpose to run a business without any data disruption. Data storage is expensive, complicated, and crucial but processing the data is considerably more so. 

As a result, it is critical to outline your company’s data goals ahead of time, such as “What data sources do you currently have and how are you currently using data?”

What information do you have and want to get for further findings?

Do you wish to learn more about your clientele, for example, or are you taking preventative measures to prevent fraud?

Do you have business goals in place that align with your big data strategy?

Importance and Integrity of Data Evaluation

Data analysts are responsible for determining the true worth of your data. How did the data come about? Inaccurate, flawed, and worthless information may have been gathered haphazardly.

It has been discovered that two-thirds of sales leads do not close due to poor data quality and that up to 25% of client and prospect records have important data inaccuracies that have a direct impact on sales.

So, before spending time and money on data analysis, data professionals check the accuracy of your information. This will assist you in determining whether the data will provide any useful insights. If it does not, gather more accurate data before proceeding.

Thriving Job Market in Data Analytics and Interpretation

Data analytics is used to find and interpret significance in data patterns. As data becomes more crucial in business decision-making, the data analytics employment market is growing.

Global firms will regard data analytics and predictive analytics to be critical components of their operations. Furthermore, as more businesses engage in data and digital assets, the demand for data analysts and experts is likely to grow.

Whether you want to enter the data analytics industry as a recent graduate, make a career shift, or brush up on your abilities, it’s critical to grasp the current environment of the data analytics employment market:

The global market for data analytics is expanding daily. The market is predicted to increase at a compound annual growth rate (CAGR) over the next half-decade, reaching considerable levels by 2028.

It is projected to maintain its global market leadership.

Job Categories in Data Analytics

Data analytics is diverse, despite its fundamental concept of analyzing data to extract information, insights, and forecasts. To get the required results, data analytics frequently overlaps with other disciplines.

Finance, marketing, and business skills, for example, can be integrated with data analytics to provide extremely targeted insights and predictions. These tasks are carried out by:

  • Analysts in Finance
  • Analysts in marketing
  • Analysts of business intelligence (BI)

In terms of the technical aspects of data analytics, computer scientists and engineers might serve as data engineers, IT systems analysts, and data architects, assisting in the organization and usage of data.

Other Forms of Data Analytics Jobs Include:

  • Transportation logistics experts
  • Analysts of operations
  • Analysts of quantitative data
  • Consultants in data analytics
  • Managers of large-scale projects

IIM SKILLS vs. IIT Madras Data Analytics Course – Overview

IIM SKILLS

IIM Skills conducts online courses in Data Analytics for students looking for reputed educational institutes with proper experts’ attention. IIM Skills provides exceptional support to its students throughout the course journey and even after completion of the course with lifetime access to premium study materials.

With the help of qualified experts enrolled can achieve milestones in the thriving job market of data analytics. IIM Skills strongly believes in quality in education delivery. This is the reason the curriculum of IIM skills is uniquely designed by well-experienced industry experts and includes complete modules of the real world. Students will get assignments and projects based on real case studies, and this sort of learning process will aid them in observing their progress and improving their flaws diligently.

The Data Analyst Course From IIM Skills Will Provide Mastery of These Skills:

    • Advanced Excel knowledge 
    • In-depth knowledge of Power BI 
    • Learn to understand the development of Python program
    • Comprehensive knowledge of the SQL program
    • Learn to interact with extensive data variables
  • Be familiar with all advanced concepts of data manipulation and complex data functions.

Course Module

Advanced Excel Knowledge

  • Data Supervision
  • Purpose and result for manipulating Data
  • Data handling and communication
  • Data conception in advanced Excel use
  • Installation of dashboards, charts, graphs, and variables to analyze data

Basic Visual Application

  • Introduction and comprehensive use of VBA (Visual Basic for Applications) in data analytics
  • Foundation and Fundamental Principles of Programming Language 
  • Setting Up and & Procedures in Visual Basic for Applications) 
  •  Design & Memory Management 
  • Code comprehensibility

Description and incorporation for SQL Server

  • Concept Introduction of the SSRS
  • Visualization Framework
  • SSRS Visual guidelines
  • Apply SSIS to Understand Project Building

Data Manoeuvring by Using Python

  • Data Manoeuvring by Using Python 
  • Summing up standard Statistics
  • Advanced use of Mathematics in data analysis
  • Python Use in Data Audit
  • NumPy (Numerical Python) Function
  • Precise knowledge of Pandas
  • Use of Python Data Wrangling, research, Visualization, and exploration of Statistical Techniques
  • Inspection of Modeling and the Data solution
  • Data Analysis arrangement
  • Reports expression (Power BI Visuals)

Tableau

  • Data Brain Storming and Account Writing

Tools Included in Advanced Data Analytics Syllabus

  • Excel: IIM Skills includes comprehensive advanced Excel knowledge for its students so they can obtain and enhance their useful understanding of Excel. Excel is a great help in getting business insights along with other data analytics tools like Power BI, and Tableau. Students will be familiarized with the complex use of Excel for data analysis. They can interpret data queries, language, and complex formulae easily in a friendly class environment. Advanced Excel helps to store extensive research, current market tracking patterns, trends, outline data, and information on campaigns.
  • Tableau: Tableau is yet another beneficial tool for data brainstorming and manipulation. Professionals may design to prepare data and share data comprehensive data insights.
  • Power BI: Power BI is an additional tool known to be useful for cloud-based analysis and explanation. This tool is a product of Microsoft and produces data perception for aiding business purposes. Power BI synthesizes variations of data from various point sources to make a genuine version an information on behalf of the company. 
  • Python: Python is a programming language and it is useful to interpret data and automate tasks in a user-friendly manner. Data Analysts use it for collecting, manipulating, and visualizing data. It is very useful in the banking sector to detect deceptive transactions.

Course Fee: ₹ 49,900 + 18% GST.

Discount: Group discount of 10% on 3 or more registrations.

Internship:2 Months Internship

Placement: Support in placements and Interviews

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IIT Madras (IITM Pravartak)

IITM Pravartak is an approved institution from the Government of India’s Department of Science and Technology. The institution aims to be part of its National Mission on Interdisciplinary Cyber-Physical Systems for comprehensive technology Mission. The further objective of the institute is to achieve total integration with all participants by founding strong links between academia, industry, government, and international organizations. Through intensive and application-oriented research, the IITM Pravartak Technology Innovation Hub focuses on new knowledge in the SNACS field. The IITM-PTF joyfully accepts the responsibility of training young India for the next generation of world-class technologies.

The Data Analyst Course From IITM Pravartak Will Provide Mastery of These Skills:

  • Microsoft Excel 
  • SQL learning for data analysis
  • Introduction to data science
  • Introduction of statistics
  • Data analysis on real projects 
  • Technical evaluation, and project assessment
  • Case studies based on Business
  • Power BI
  • Machine learning techniques

Course Module

Data Transformation (using MS Excel and Python)

Learn data transformation, and data analysis functions to discover the fundamentals of data analytics by using Python and Excel. 

Data operations with SQL

  • Introduction of SQL
  • Database regulations
  • Individual correlation model
  • SQL mechanismTables, charts, graphs, and variables
  • SQL performance, functional views, and stored method
  • SQL function and optimization
  • Advanced analytical concepts
  • Grouping sets

Development of Data Science and Statistics

  • Mean approaches
  • Median methods
  • Mode variations
  • Tables and charts

Business Management, Insights, and problem-solving

  • Understanding the financial problem to resolve business issues
  • Exploratory data analysis
  • Discussion on real-life marketing cases
  • Project on finance, marketing, and business insights

Optimization Techniques

  • Goal-oriented programming
  • Integer programming
  • Mixed integer programming
  • Distribution and networking

Statistics, Data Mining and Machine Learning using R Programming

  • Use of R
  • Advanced statistics
  • Technical analysis
  • Clustering
  • Decision tree

Advance Machine Learning

  • Advanced machine learning-XGBoost
  • Problem-solving
  • Parameter optimization

Visualization with the use of Power BI

    • Power BI Introduction
    • Advanced learning of Power BI
    • Detailed data extraction and insights
    • Data transformation
    • Data analysis and visualization
  • Power BI setting, and connectivity

Program Name: B.Sc. in Programming and Data Science

Duration: 3 years (Maximum limit to complete in 6 years)

Conclusion of This Well-detailed Read – IIM SKILLS vs. IIT Madras Data Analytics Course

 IIM SKILLS vs. IIT Madras (IITM Pravartak) Data Analytics Course is an article about data analytics courses. We tried to give you a synopsis of the most demanding courses in the current time of technical development. Data Analytics has a promising present and future in the thriving job market. 

Data analysis is critical for making educated business decisions, and the demand for data analysts is expected to rise dramatically in the future years. Jobs for young and experienced data professionals are expected to grow. The best understanding of the course details and a clear roadmap for imminent opportunities and the curriculum by experts will surely be a great help to prospective professionals.

Frequently Asked Questions (FAQs) related to IIM SKILLS vs. IIT Madras Data Analytics Course

Question 1) Who is eligible for the data analytics course?

Answer: Data Analytics courses can be opted for anyone such as:

  • Non-IT Professionals 
  • Professionals from every sector such as banking, marketing, and 
  • Data Analytics Professionals
  • Project Managers
  • Software Developers
  • Information Architects
  • Beginners, Graduates from any disciples
  • Individuals who are looking for a career change as well

Question 2) How many programming languages are essential parts of the data analytics syllabus?

Answer: There are several different data analysis programming languages to choose from as a professional in this field. There are some of the top five data analyst languages to use in data analysis such as:

  • Data analysis is critical for making educated choices in business decisions, and the demand for data analysts is expected to rise dramatically in the future years.
  • Python, R, SQL, Java, and Scala are the top five data analysis programming languages.
  • Python is a versatile, object-oriented programming language that is frequently used for data analysis, but R excels at statistical analysis and data visualization.
  • There is no less or wrong programming language to learn for data analysis because every language has significance in the computer business.
  • Begin by studying the principles and d data analysis tools such as Excel, Python, R, and querying languages.
  • Khan Academy, Kaggle, KDnuggets, GitHub, and DataCamp are some of the best tools for studying data analysis.

Computer systems receive instructions from language codes. A high-level programming language is very informative and usually easier to read as well as write than a low-level programming language. The source code of high-level languages is written in an easy-to-read syntax.

C, C++, Java, and JavaScript are popular high-level languages. Processors are capable of running low-level languages without the use of an interpreter. These are machine languages that computers can understand.

Question 3) What is the difference between Python and R languages in data analytics?

Answer: Python and R both have an essential need in data analysis to accomplish the process. For example, R is a beneficial choice for visualizing and statistical data analysis. On the other hand, Python is a more multi-purpose, adaptable language and can be used for more recreational and genuine tasks in data analytics.

Description of Python and R

Python

Python is expected to be one of the most popular programming languages in the future. It can be used to extract information, write code, and build websites.

To use Python for this purpose, you may need to download libraries to reduce the amount of coding required. The programming language has a wide range of applications and is user-friendly for beginners.

R

R is a data analysis programming language. This simple language requires fewer libraries than Python and lets you detect trends and patterns in your data. It may be used to create spectacular data visualizations as well as statistical models.

R is used by data analysts because it provides statistical packages for quantitative applications. Neural networks, phylogenetics, sophisticated charting, and nonlinear regression are among them. R is an open-source language that is also designed to be adaptable.

Differences

  • Differences in R and Python:  The two core programming languages.
  • Learning challenges in data analysis: Python is an excellent choice for beginning programmers, although R can be significantly more difficult to master.
  • Upkeep issues: One of the most unavoidable differences between R and Python is that Python code requires less maintenance than R code.
  • Library power: R has fewer and less sophisticated libraries than Python, making this part of the programming language simpler to understand.
  • Applications:  As previously stated, the two languages offer distinct advantages. R is better for complex statistical calculations, whereas Python is better for deep learning.

 

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