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Description

[November 2024 update]
Added a video on the "Analyze Data" option in MS Excel
Added a video on "How Index works in SQL"
Added a new section on how to practice SQL using fun case studies.
Added a video on "How to integrate ChatGPT inside Excel using openAI API"
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!
Cheers
Start-Tech Academy
Who this course is for:
Anyone curious to master Data Analysis in a short span of time
Business Analysts/ Managers who want to expand on the current set of skills

What you'll learn

A Beginner's Guide to Microsoft Excel - Microsoft Excel, Learn Excel, Spreadsheets, Formulas, Shortcuts, Macros

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

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

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

Knowledge of all the essential SQL commands

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 problem using the Linear Regression technique

Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem

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

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction
2
1.1-Introduction
1.2-Course Resources
2-Excel Basics
7
2.1-Milestone!
2.2-Basics
2.3-Worksheet Basics
2.4-Data Formats
2.5-Data Handling Basics - Cut, Copy and Paste
2.6-Saving and Printing - Basics
2.7-Quiz
3-Essential Formulas
17
3.1-Basic Formula Operations
3.2-Mathematical Functions Part-1
3.3-Mathematical Functions Part-2
3.4-Quiz
3.5-Difference between RANK, RANK.AVG and RANK.EQ
3.6-Exercise 1: Mathematical Functions
3.7-Textual Functions Part -1
3.8-Textual Functions Part -2
3.9-Exercise 2: Textual Functions
3.10-Quiz
3.11-Logical Functions
3.12-Exercise 3: Logical Functions
3.13-Date-Time Functions
3.14-Exercise 4: Date-Time Functions
3.15-Lookup Functions (V Lookup, Hlookup, Index-Match)
3.16-Exercise 5: Lookup Functions
3.17-Quiz
4-XLookup - only for Excel 2021 and Office 365
4
4.1-XLookup as a replacement of Vlookup
4.2-Handling #NA and Approximates match in Xlookup
4.3-Wildcard matching in XLookup
4.4-Search modes in XLookup
5-Data Tools
7
5.1-Sorting, Filtering and Data Validation
5.2-Text-to-columns and remove duplicates
5.3-Advanced Filter option
5.4-Exercise 6: Data Tools
5.5-Formatting data and tables
5.6-Exercise 7: Formatting
5.7-Quiz
6-Excel Charts
14
6.1-Importance of data visualization
6.2-Elements of charts
6.3-The Easy way of creating charts
6.4-Bar and column charts
6.5-Formatting Charts Part 1
6.6-Formatting Charts Part 2
6.7-Line Charts
6.8-Area Charts
6.9-Pie and Doughnut Charts
6.10-Scatter plot or XY chart
6.11-Waterfall Charts
6.12-Sparklines
6.13-Exercise 8: Charts
6.14-Quiz
7-Pivot table and Pivot charts
3
7.1-Pivot Tables
7.2-Exercise 9: Pivot tables
7.3-Pivot Charts
8-NEW! Analyze Data option in Excel - only for Microsoft 365 users
1
8.1-Analyze Data option in Excel
9-Maps Chart - only for Excel 2019 and above
1
9.1-Maps chart
10-Using ChatGPT with Excel
2
10.1-Use ChatGPT for Excel problems
10.2-Integrate ChatGPT with Excel using API
11-Macros
3
11.1-Macros
11.2-Exercise 10: Macros
11.3-Quiz
12-Importing data from External Sources
4
12.1-Importing tables from PDF - Excel 2021 and office 365
12.2-Importing data from Websites - Excel 2019 and office 365
12.3-Practical Task 1
12.4-Quiz
13-SQL Introduction
1
13.1-Introduction
14-Installation and getting started
2
14.1-Installing PostgreSQL and pgAdmin in your System
14.2-If pgAdmin is not opening...
15-Database Basics
5
15.1-What is SQL
15.2-Tables and DBMS
15.3-Types of SQL commands
15.4-PostgreSQL
15.5-Quiz
16-Fundamental SQL statements
25
16.1-CREATE
16.2-Exercise 1: Create DB and Table
16.3-Solutions to all Exercises
16.4-INSERT
16.5-PRIMARY KEY FOREIGN KEY
16.6-Import data from File
16.7-Exercise 2 Inserting and Importing data
16.8-SELECT statement
16.9-Quick coding exercise on Select Statement
16.10-SELECT DISTINCT
16.11-Quick coding exercise on Distinct Command
16.12-WHERE
16.13-Quick coding exercise on Where Statement
16.14-Logical Operators
16.15-Quick coding exercise on Logical Operators
16.16-Exercise 3 SELECT, WHERE & Logical
16.17-UPDATE
16.18-Quick coding exercise on Update Command
16.19-DELETE
16.20-Quick coding exercise on Delete Command
16.21-ALTER Part 1
16.22-ALTER Part 2
16.23-Quick coding exercise on Alter Command
16.24-Exercise 4 Update, Delete and Alter Table
16.25-Quiz
17-Restore and Back-up
5
17.1-Restore and Back-up
17.2-Debugging restoration issues
17.3-Creating DB using CSV files
17.4-Debugging summary and Code for CSV files
17.5-Exercise 5 Restore and Back-up
18-Selection commands: Filtering
8
18.1-IN
18.2-Quick coding exercise on IN operator
18.3-BETWEEN
18.4-Quick coding exercise on Between Operator
18.5-LIKE
18.6-Quick coding exercise on Like operator
18.7-Exercise 6: In, Like & Between
18.8-Quiz
19-Selection commands: Ordering
7
19.1-Side Lecture Commenting in SQL
19.2-ORDER BY
19.3-Quick coding exercise on Order by Clause
19.4-LIMIT
19.5-Quick coding exercise on Limit Command
19.6-Exercise 7 Sorting
19.7-Quiz
20-Alias
2
20.1-AS
20.2-Quick coding exercise on AS operator
21-Aggregate Commands
10
21.1-COUNT
21.2-Quick coding exercise on Count function
21.3-SUM
21.4-Quick coding exercise on Sum function
21.5-AVERAGE
21.6-Quick coding exercise on Average function
21.7-MIN MAX
21.8-Quick coding exercise on MIN & MAX function
21.9-Exercise 8 Aggregate functions
21.10-Quiz
22-Group By Commands
6
22.1-GROUP BY
22.2-Quick coding exercise on Group By Clause
22.3-HAVING
22.4-Quick coding exercise on Having Clause
22.5-Exercise 9 Group By
22.6-Quiz
23-Conditional Statement
2
23.1-CASE WHEN
23.2-Quick coding exercise on CASE WHEN Statement
24-JOINS
21
24.1-Introduction to Joins
24.2-Concepts of Joining and Combining Data
24.3-Preparing the data
24.4-Inner Join
24.5-Quick coding exercise on Inner Join
24.6-Left Join
24.7-Quick coding exercise on Left Join
24.8-Right Join
24.9-Quick coding exercise on Right Join
24.10-Full Outer Join
24.11-Quick coding exercise on Full Outer Join
24.12-Cross Join
24.13-Quick coding exercise on Cross Join
24.14-Intersect and Intersect ALL
24.15-Quick coding exercise on Intersect and Intersect ALL
24.16-Except
24.17-Quick coding exercise on Except
24.18-Union
24.19-Quick coding exercise on Union Operator
24.20-Exercise 10 Joins
24.21-Quiz
25-Subqueries
7
25.1-Part-1 Subquery in WHERE clause
25.2-Quick coding exercise on Subquery in Where Clause
25.3-Part-2 Subquery in FROM clause
25.4-Quick coding exercise on Subquery in From Clause
25.5-Part-3 Subquery in SELECT clause
25.6-Quick coding exercise on Subquery in Select Clause
25.7-Exercise 11 Subqueries
26-Fun way to practice SQL
1
26.1-Solve Murder Mystery using SQL
27-Views and Indexes
7
27.1-VIEWS
27.2-Quick coding exercise on Views
27.3-INDEX
27.4-Quick coding exercise on Index
27.5-How Index works in SQL
27.6-Exercise 12 Views
27.7-Quiz
28-String Functions
14
28.1-LENGTH
28.2-Quick coding exercise on LENGTH function
28.3-UPPER LOWER
28.4-Quick coding exercise on UPPER LOWER function
28.5-REPLACE
28.6-Quick coding exercise on REPLACE function
28.7-TRIM LTRIM RTRIM
28.8-Quick coding exercise on TRIM, LTRIM, RTRIM functions
28.9-CONCATENATION
28.10-Quick coding exercise on CONCATENATION function
28.11-SUBSTRING
28.12-Quick coding exercise on SUBSTRING function
28.13-LIST AGGREGATION
28.14-Exercise 13 String Functions
29-Mathematical Functions
10
29.1-CEIL FLOOR
29.2-Quick coding exercise on CEIL & FLOOR functions
29.3-RANDOM
29.4-SETSEED
29.5-ROUND
29.6-Quick coding exercise on ROUND function
29.7-POWER
29.8-Quick coding exercise on POWER function
29.9-Exercise 14 Mathematical Functions
29.10-Quiz
30-Date-Time Functions
6
30.1-CURRENT DATE TIME
30.2-Quick coding exercise on CURRENT DATE & TIME function
30.3-AGE
30.4-EXTRACT
30.5-Exercise 15 Date-time functions
30.6-Quiz
31-PATTERN (STRING) MATCHING
6
31.1-PATTERN MATCHING BASICS
31.2-Quick coding exercise on Pattern Matching Basics
31.3-ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS) Part 1
31.4-ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS) Part 2
31.5-Exercise 16 Pattern Matching
31.6-Quiz
32-Window Functions
17
32.1-Introduction to window functions
32.2-Introduction to row numbers
32.3-Implementing row numbers in SQL
32.4-Quick coding exercise on Row number
32.5-RANK and DENSERANK
32.6-Quick coding exercise on RANK and DENSERANK
32.7-NTILE
32.8-Quick coding exercise on NTILE function
32.9-AVERAGE
32.10-Quick coding exercise on AVERAGE function
32.11-COUNT
32.12-Quick coding exercise on COUNT function
32.13-SUM TOTAL
32.14-Quick coding exercise on SUM TOTAL function
32.15-Running Total
32.16-LAG and LEAD
32.17-Quick coding exercise on LAG and LEAD function
33-Data Type conversion functions
5
33.1-Converting Numbers Date to String
33.2-Quick coding exercise on Converting Numbers/ Date to String function
33.3-Converting String to Numbers Date
33.4-Quick coding exercise on Converting String to Numbers/ Date function
33.5-Practical Task 1
34-Showcasing SQL Skills with HackerRank Stars
1
34.1-Showcasing SQL Skills with HackerRank Stars
35-Introduction to Linear Regression
1
35.1-Welcome to the module
36-Setting up Python and Jupyter Notebook
19
36.1-Installing Python and Anaconda
36.2-Opening Jupyter Notebook
36.3-Introduction to Jupyter Notebook Part 1
36.4-Introduction to Jupyter Notebook Part 2
36.5-Arithmetic operators in Python Python Basics
36.6-Quick coding exercise on arithmetic operators
36.7-Strings in Python Part 1
36.8-Strings in Python Part 2
36.9-Quick coding exercise on String operations
36.10-Lists Tuples and Directories Part 1
36.11-Lists Tuples and Directories Part 2
36.12-Lists Tuples and Directories Part 3
36.13-Quick coding exercise on Tuples
36.14-Working with Numpy Library of Python
36.15-Quick coding exercise on NumPy Library
36.16-Working with Pandas Library of Python
36.17-Quick coding exercise on Pandas Library
36.18-Working with Seaborn Library of Python
36.19-Python file for additional practice
37-Integrating ChatGPT with Python
1
37.1-Integrating ChatGPT with Jupyter notebook
38-Basics of Statistics
5
38.1-Types of Data
38.2-Types of Statistics
38.3-Describing data Graphically
38.4-Measures of Centers
38.5-Measures of Dispersion
39-Introduction to Machine Learning
2
39.1-Introduction to Machine Learning
39.2-Building a Machine Learning Model
40-Data Preprocessing
18
40.1-Gathering Business Knowledge
40.2-Data Exploration
40.3-The Dataset and the Data Dictionary
40.4-Importing Data in Python
40.5-Univariate analysis and EDD
40.6-EDD in Python
40.7-Outlier Treatment
40.8-Outlier Treatment in Python
40.9-Missing Value Imputation
40.10-Missing Value Imputation in Python
40.11-Seasonality in Data
40.12-Bi-variate analysis and Variable transformation
40.13-Variable transformation and deletion in Python
40.14-Non-usable variables
40.15-Dummy variable creation Handling qualitative data
40.16-Dummy variable creation in Python
40.17-Correlation Analysis
40.18-Correlation Analysis in Python
41-Linear Regression
19
41.1-The Problem Statement
41.2-Basic Equations and Ordinary Least Squares (OLS) method
41.3-Assessing accuracy of predicted coefficients
41.4-Assessing Model Accuracy RSE and R squared
41.5-Simple Linear Regression in Python
41.6-Multiple Linear Regression
41.7-The F - statistic
41.8-Interpreting results of Categorical variables
41.9-Multiple Linear Regression in Python
41.10-Test-train split
41.11-Bias Variance trade-off
41.12-More about test-train split
41.13-Test train split in Python
41.14-Linear models other than OLS
41.15-Subset selection techniques
41.16-Shrinkage methods Ridge and Lasso
41.17-Ridge regression and Lasso in Python
41.18-Comprehensive Interview Preparation Questions
41.19-The final milestone!
42-Congratulations & about your certificate
2
42.1-About your certificate
42.2-Bonus Lecture