• Post category:Udemy (Feb 2022)
  • Reading time:19 mins read


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

What you will 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 |

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.


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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

हिन्दी
language

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
SELECT DISTINCT
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
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

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