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Begin data analytics by learning Excel, SQL, Python, Analytics & ML concepts from scratch. Must-know for a data analyst

What you will 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

Description

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.


Get Instant Notification of New Courses on our Telegram channel.


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 – SQLΒ 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

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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 Part-1
Mathematical Functions Part-2
Difference between RANK, RANK.AVG and RANK.EQ
Textual Functions Part -1
Textual Functions Part -2
Logical Functions
Date-Time Functions
Lookup Functions (V Lookup, Hlookup, Index-Match)
Data Tools
Data Tools Part – 1
Data Tools Part – 2
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 Part 1
Formatting Charts Part 2
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…
Database Basics
What is SQL
Tables and DBMS
Types of SQL commands
PostgreSQL
Fundamental SQL statements
CREATE
Exercise 1 Create DB and Table
Solutions to all Exercises
INSERT
PRIMARY KEY FOREIGN KEY
Import data from File
Exercise 2 Inserting and Importing
SELECT statement
SELECT DISTINCT
WHERE
Logical Operators
Exercise 3 SELECT WHERE
UPDATE
DELETE
ALTER Part 1
ALTER Part 2
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
Selection commands: Filtering
IN
BETWEEN
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
AVERAGE
MIN MAX
Exercise 8 Aggregate functions
Group By Commands
GROUP BY
HAVING
Exercise 9 Group By
Conditional Statement
CASE WHEN
JOINS
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
UPPER LOWER
REPLACE
TRIM LTRIM RTRIM
CONCATENATION
SUBSTRING
LIST AGGREGATION
Exercise 13 String Functions
Mathematical Functions
CEIL FLOOR
RANDOM
SETSEED
ROUND
POWER
Exercise 14 Mathematical Functions
Date-Time Functions
CURRENT DATE TIME
AGE
EXTRACT
Exercise 15 Date-time functions
PATTERN (STRING) MATCHING
PATTERN MATCHING BASICS
ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS) Part 1
ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS) Part 2
Exercise 16 Pattern Matching
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 Jupyter Notebook
Introduction to Jupyter Notebook Part 1
Introduction to Jupyter Notebook Part 2
Arithmetic operators in Python Python Basics
Strings in Python Part 1
Strings in Python Part 2
Lists Tuples and Directories Part 1
Lists Tuples and Directories Part 2
Lists Tuples and Directories Part 3
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
Building a Machine Learning Model
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
Final Section
The final milestone!
Congratulations & About your certificate