🇮🇳 Data Analyst Skill path in Hindi – हिंदी में Data analytics

🇮🇳 Data Analyst Skill path in Hindi - हिंदी में Data analytics

Data Analyst Skill path in Hindi – हिंदी में Data analytics

Learn data analytics by learning Excel, SQL, Python, Analytics & ML concepts from scratch in Hindi

Language: hindi

Note: 4.5/5 (260 notes) 15,324 students

Instructor(s): Start-Tech Trainings

Last update: 2022-08-19

What you’ll learn

  • Microsoft Excel सीखने वाले सभी नए विद्यार्थियों के लिए कोर्स – इसमें आप Microsoft Excel, Spreadsheets, Formulas, Excel shortcuts, Macros आदि सीखेंगे
  • Excel के सबसे ज़रूरी और लोकप्रिय Lookup फंक्शन जैसे – Vlookup, Hlookup, Index और Match फंक्शनों को काफी अच्छे से सीख पाओगे ।
  • SQL की सभी ज़रूरी commands को सीख पाओगे |
  • Bar chart, Scatter plots, Histogram आदि का उपयोग करके अपने दर्शकों को आकर्षित कर पाओगे ।
  • Machine Learning Linear Regression problem के लिए data collection और data preprocessing की गहराई से जानकारी प्राप्त करोगे |



  • Only a PC with any version of Excel installed is needed for this course on “Data Analyst Skillpath: Zero to Hero in Excel, SQL & Python””



You’re looking for a complete course on how to become a data analyst, right?

You’ve found the right Data Analyst Masterclass with Excel, SQL & Python course! This course will teach you data-driven decision-making, data visualization, data analytics in SQL, and the use of predictive analytics like linear regression in business settings.

After completing this course you will be able to:

  • Master Excel’s most popular lookup functions such as Vlookup, Hlookup, Index, and Match

  • Become proficient in Excel data tools like Sorting, Filtering, Data validations, and Data importing

  • Make great presentations using Bar charts, Scatter Plots, Histograms, etc.

  • Become proficient in SQL tools like GROUP BY, JOINS, and Subqueries

  • Become competent in using sorting and filtering commands in SQL

  • Learn how to solve real-life business problems using the Linear Regression technique

  • Understand how to interpret the result of the Linear Regression model and translate them into actionable insight

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this course on Data Analyst Skillpath in Excel, SQL, and Python.

If you are a student, business manager, or business analyst, or an executive who wants to learn Data Analytics concepts and apply data analytics techniques to real-world problems of the business function, this course will give you a solid base for Data Analytics by teaching you the most popular data analysis models and tools

Why should you choose this course?

We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:

  • Concepts and use cases of different Statistical tools required for evaluating data analytics models

  • Step-by-step instructions on implementing data analytics models

  • Downloadable files containing data and solutions used in the course

  • Class notes and assignments to revise and practice the concepts

The practical classes where we create the model for each of these strategies are something that differentiates this course from any other course available online.

What makes us qualified to teach you?

The course is taught by Abhishek (MBA – FMS Delhi, B. Tech – IIT Roorkee) and Pukhraj (MBA – IIM Ahmedabad, B. Tech – IIT Roorkee). As managers in the Global Analytics Consulting firm, we have helped businesses solve their business problems using Analytics and we have used our experience to include the practical aspects of business analytics in this course. We have in-hand experience in Business Analysis.

We are also the creators of some of the most popular online courses – with over 1,200,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman – Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet, or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts like Data Analytics in MS Excel, SQL, and Python. Each section contains a practice assignment for you to practically implement your learning on Data Analytics.

What is covered in this course?

The analysis of data is not the main crux of analytics. It is the interpretation that helps provide insights after the application of analytical techniques that makes analytics such an important discipline. We have used the most popular analytics software tools which are MS Excel, SQL, and Python. This will aid the students who have no prior coding background to learn and implement Analytics and Machine Learning concepts to actually solve real-world problems of Data Analysis.

Let me give you a brief overview of the course

  • Part 1 – Excel for data analytics

In the first section, i.e. Excel for data analytics, we will learn how to use excel for data-related operations such as calculating, transforming, matching, filtering, sorting, and aggregating data.

We will also cover how to use different types of charts to visualize the data and discover hidden data patterns.

  • Part 2 – SQL for data analytics

IN the second section, i.e. SQL for data analytics, we will be teaching you everything in SQL that you will need for Data analysis in businesses. We will start with basic data operations like creating a table, retrieving data from a table etc. Later on, we will learn advanced topics like subqueries, Joins, data aggregation, and pattern matching.

  • Part 3 – Preprocessing Data for ML models

In this section, you will learn what actions you need to take step by step to get the data and then prepare it for analysis, these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation, and correlation.

  • Part 4 – Linear regression model for predicting metrics

This section starts with simple linear regression and then covers multiple linear regression.

We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

I am pretty confident that the course will give you the necessary knowledge on Data Analysis, and the skillsets of a Data Analyst to immediately see practical benefits in your workplace.

Go ahead and click the enroll button, and I’ll see you in lesson 1 of this Data Analyst Skillpath course!


Start-Tech Academy


Who this course is for

  • कोई भी विद्यार्थी जो काम समय में Data Analysis को अच्छे से सीखना चाहता है|
  • Business Analysts/ Managers who want to expand on the current set of skills


Course content

  • Introduction
    • Introduction
    • Course Resources
  • Excel Basics
    • Basics
    • Milestone!
    • Worksheet Basics
    • Data Formats
    • Data Handling Basics – Cut, Copy and Paste
    • Saving and Printing – Basics
  • Essential Formulas
    • Basic Formula Operations
    • Mathematical Functions
    • Difference between RANK, RANK.AVG and RANK.EQ
    • Textual Functions
    • Logical Functions
    • Date-Time Functions
    • Lookup Functions (V Lookup, Hlookup, Index-Match)
  • Data Tools
    • Data Tools
    • Formatting data and tables
  • Excel Charts
    • Importance of data visualization
    • Elements of charts
    • The Easy way of creating charts
    • Bar and column charts
    • Formatting Charts
    • Line Charts
    • Area Charts
    • Pie and Doughnut Charts
    • Scatter plot or XY chart
    • Waterfall Charts
    • Sparklines
  • Pivot table and Pivot charts
    • Pivot Tables
    • Pivot Charts
  • Macros
    • Macros
  • SQL Introduction
    • Introduction
  • Installation and getting started
    • Installation
    • If pgAdmin is not opening…
  • Case study
    • Case Study part:1
    • Case Study part: 2
  • Fundamental SQL statements
    • CREATE
    • Exercise 1 Create DB and Table
    • Solutions to all Exercises
    • INSERT
    • Import data from File
    • Exercise 2 Inserting and Importing
    • SELECT statement
    • WHERE
    • Logical operators
    • Exercise 3 SELECT WHERE
    • UPDATE
    • DELETE
    • ALTER
    • Exercise 4 Updating Table
  • Restore and Back-up
    • Restore and Back-up
    • Debugging restoration issues
    • Creating DB using CSV files
    • Debugging summary and Code for CSV files
    • Exercise 5 Restore and Back-up
  • Section commands: Filtering
    • IN
    • LIKE
    • Exercise 6: In, Like & Between
  • Selection commands: Ordering
    • Side Lecture Commenting in SQL
    • ORDER BY
    • LIMIT
    • Exercise 7 Sorting
  • Alias
    • AS
  • Aggregate Commands
    • Count
    • SUM
    • MIN MAX
    • Exercise 8 Aggregate functions
  • Group By Commands
    • GROUP BY
    • HAVING
    • Exercise 9 Group By
  • Conditional Statement
    • Introduction to Joins
    • Concepts of Joining and Combining Data
    • Preparing the data
    • Inner Join
    • Left Join
    • Right Join
    • Full Outer Join
    • Cross Join
    • Intersect and Intersect ALL
    • Except
    • Union
    • Exercise 10 Joins
  • Subqueries
    • Part-1 Subquery in WHERE clause
    • Part-2 Subquery in FROM clause
    • Part-3 Subquery in SELECT clause
    • Exercise 11 Subqueries
  • Views and Indexes
    • VIEWS
    • INDEX
    • Exercise 12 Views
  • String Functions
    • LENGTH
    • Exercise 13 String Functions
  • Mathematical Functions
    • RANDOM
    • ROUND
    • POWER
    • Exercise 14 Mathematical Functions
  • Date-Time Functions
    • AGE
  • Data Type conversion functions
    • Converting Numbers Date to String
    • Converting String to Numbers Date
  • Introduction to Linear Regression
    • Welcome to the module
  • Setting up Python and Jupyter Notebook
    • Installing Python and Anaconda
    • Opening Google colab
    • Arithmetic operators in Python Python Basics
    • Strings in Python Part 1
    • Lists Tuples and Directories Part 1
    • Working with Numpy Library of Python
    • Working with Pandas Library of Python
    • Working with Seaborn Library of Python
  • Basics of Statistics
    • Types of Data
    • Types of Statistics
    • Describing data Graphically
    • Measures of Centers
    • Measures of Dispersion
  • Introduction to Machine Learning
    • Introduction to Machine Learning
  • Data Preprocessing
    • Gathering Business Knowledge
    • Data Exploration
    • The Dataset and the Data Dictionary
    • Importing Data in Python
    • Univariate analysis and EDD
    • EDD in Python
    • Outlier Treatment
    • Outlier Treatment in Python
    • Missing Value Imputation
    • Missing Value Imputation in Python
    • Seasonality in Data
    • Bi-variate analysis and Variable transformation
    • Variable transformation and deletion in Python
    • Non-usable variables
    • Dummy variable creation Handling qualitative data
    • Dummy variable creation in Python
    • Correlation Analysis
    • Correlation Analysis in Python
  • Linear Regression
    • The Problem Statement
    • Basic Equations and Ordinary Least Squares (OLS) method
    • Assessing accuracy of predicted coefficients
    • Assessing Model Accuracy RSE and R squared
    • Simple Linear Regression in Python
    • Multiple Linear Regression
    • The F – statistic
    • Interpreting results of Categorical variables
    • Multiple Linear Regression in Python
    • Test-train split
    • Bias Variance trade-off
    • More about test-train split
    • Test train split in Python
    • Linear models other than OLS
    • Subset selection techniques
    • Shrinkage methods Ridge and Lasso
    • Ridge regression and Lasso in Python
    • The final milestone!
  • Congratulations & about your certificate
    • Bonus Lecture


🇮🇳 Data Analyst Skill path in Hindi - हिंदी में Data analytics🇮🇳 Data Analyst Skill path in Hindi - हिंदी में Data analytics

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