Course / Course Details

Deep dive with infotact on Data Analytics

  • Smrutirupa Parida image

    By - Smrutirupa Parida

  • 72 students
  • 94 Hours 4 Min
  • (0)

Course Requirements

  • Basic understanding of computers and internet usage
  • Familiarity with Excel or spreadsheets (recommended but not mandatory)
  • Basic logical and analytical thinking
  • No prior coding experience required (beginner-friendly)
  • Laptop/desktop with stable internet connection
  • Interest in working with data and deriving insights 

Course Description

The Deep Dive with Infotact on Data Analytics is a practical, industry-focused program designed to help learners understand how to collect, process, analyze, and visualize data to support decision-making.

This course introduces learners to the fundamentals of data analytics, including data cleaning, transformation, visualization, and reporting. Participants will gain hands-on experience with tools such as Excel, SQL, and Python, along with popular visualization platforms.

The program emphasizes real-world applications, enabling learners to work on datasets, build dashboards, and generate actionable insights. By the end of the course, participants will be equipped with the skills needed to begin their journey in data analytics and related roles.

Course Outcomes

By the end of this course, learners will be able to:

  • Understand the fundamentals of data analytics and its real-world applications
  • Perform data cleaning and preprocessing using Excel and Python
  • Write basic to intermediate SQL queries for data extraction and analysis
  • Conduct exploratory data analysis (EDA) to identify patterns and trends
  • Create interactive dashboards and reports using visualization tools
  • Interpret data and communicate insights effectively
  • Work with structured datasets and solve business problems
  • Understand key metrics and data-driven decision-making
  • Gain hands-on experience through real-world projects
  • Prepare for entry-level roles in Data Analytics

Course Curriculum

  • 39 chapters
  • 297 lectures
  • 0 quizzes
  • 94 Hours 4 Min total length
Toggle all chapters
1 A Practical Example - What Will You Learn in This Course
4.47 Min

2 What Does the Course Cover
4.46 Min

1 Introduction to the World of Business and Data
2.26 Min

2 Relevant Terms Explained
5.48 Min

3 Data Analyst Compared to Other Data Jobs
2.28 Min

4 Data Analyst Job Description
5.42 Min

5 Why Python
5.43 Min

1 Introduction
1.25 Min

2 001 Setting-Up-the-Environment.pdf
6 Min

3 Programming Explained in a Few Minutes
5.03 Min

4 Jupyter - Introduction
3.28 Min

5 Jupyter - Installing Anaconda
4 Min

6 Jupyter - Intro to Using Jupyter
3.1 Min

7 Jupyter - Working with Notebook Files
6.1 Min

8 Jupyter - Using Shortcuts
3.7 Min

9 Jupyter - Handling Error Messages
5.56 Min

10 Jupyter - Restarting the Kernel
2.17 Min

11 010 Jupyter-Shortcuts.pdf
5 Min

12 010 Setting-Up-the-Environment.pdf
5 Min

1 Python Variables
3.37 Min

2 001 Python-Basics.pdf
5 Min

3 Types of Data - Numbers and Boolean Values
3.04 Min

4 Types of Data - Strings
5.4 Min

5 Basic Python Syntax - Arithmetic Operators
3.23 Min

6 Basic Python Syntax - The Double Equality Sign
1.33 Min

7 Basic Python Syntax - Reassign Values
1.6 Min

8 Basic Python Syntax - Add Comments
1.34 Min

9 Basic Python Syntax - Line Continuation
1 Min

10 Basic Python Syntax - Indexing Elements
1.18 Min

11 Basic Python Syntax - Indentation
1.44 Min

12 Operators - Comparison Operators
2.11 Min

13 Operators - Logical and Identity Operators
5.35 Min

14 Conditional Statements - The IF Statement
3.02 Min

15 Conditional Statements - The ELSE Statement
2.47 Min

16 Conditional Statements - The ELIF Statement
5.34 Min

17 Conditional Statements - A Note on Boolean Values
2.14 Min

18 Functions - Defining a Function in Python
2 Min

19 Functions - Creating a Function with a Parameter
3.49 Min

20 Functions - Another Way to Define a Function
2.35 Min

21 Functions - Using a Function in Another Function
1.5 Min

22 Functions - Combining Conditional Statements and Functions
3.5 Min

23 Functions - Creating Functions That Contain a Few Arguments
1.17 Min

24 Functions - Notable Built-in Functions in Python
3.57 Min

25 Sequences - Lists
4.03 Min

26 Sequences - Using Methods
3.19 Min

27 Sequences - List Slicing
4.3 Min

28 Sequences - Tuples
3.1 Min

29 Sequences - Dictionaries
4.04 Min

30 Iteration - For Loops
2.57 Min

31 Iteration - While Loops and Incrementing
2.26 Min

32 Iteration - Create Lists with the range() Function
3.49 Min

33 Iteration - Use Conditional Statements and Loops Together
3.11 Min

34 Iteration - Conditional Statements, Functions, and Loops
2.27 Min

35 Iteration - Iterating over Dictionaries
3.07 Min

36 Python-Basics
10 Min

37 001 Python-Basics-Solutions.zip
45 Min

1 Object-Oriented Programming (OOP)
5 Min

2 Modules, Packages, and the Python Standard Library
4.24 Min

3 Importing Modules
3.24 Min

4 Introduction to Using NumPy and pandas
9.1 Min

5 What is Software Documentation
3.58 Min

6 The Python Documentation
6.24 Min

7 05 - Fundamentals for Coding in Python.zip
5 Min

1 What Is а Matrix
3.37 Min

2 Scalars and Vectors
2.59 Min

3 Linear Algebra and Geometry
3.06 Min

4 Arrays in Python
5.1 Min

5 What Is a Tensor
3 Min

6 Adding and Subtracting Matrices
3.35 Min

7 Errors When Adding Matrices
2.01 Min

8 Transpose
5.13 Min

9 Dot Product of Vectors
3.49 Min

10 Dot Product of Matrices
8.23 Min

11 Why is Linear Algebra Useful
10.1 Min

1 The NumPy Package and Why We Use It
4.03 Min

2 InstallingUpgrading NumPy
2.01 Min

3 Ndarray
3.06 Min

4 The NumPy Documentation
4.42 Min

5 005 Intro-to-NumPy-Solution.zip
5 Min

1 Introduction to the pandas Library
5.42 Min

2 001 Introduction-to-pandas-DataFrames.pdf
5 Min

3 Installing and Running pandas
5.57 Min

4 Introduction to pandas Series
8.4 Min

5 Working with Attributes in Python
5.22 Min

6 Using an Index in pandas
4 Min

7 Label-based vs Position-based Indexing
4.31 Min

8 More on Working with Indices in Python
5.37 Min

9 Using Methods in Python - Part I
4.55 Min

10 Using Methods in Python - Part II
2.36 Min

11 Parameters vs Arguments
4.35 Min

12 001 pandas-Basics-Solutions.zip
6 Min

1 The pandas Documentation
9.55 Min

2 Introduction to pandas DataFrames
5.24 Min

3 Creating DataFrames from Scratch - Part I
5.57 Min

4 Creating DataFrames from Scratch - Part II
5.03 Min

5 Additional Notes on Using DataFrames
1.58 Min

6 055 Data-Analyst-pandas-Basics-Conclusion.pdf
8 Min

7 055 pandas-Basics-Exercises.zip
6 Min

1 Common-Naming-conventions
7 Min

2 Working with Files in Python - An Introduction
3.48 Min

3 File vs File Object, Read vs Parse
2.52 Min

4 Structured vs Semi-Structured and Unstructured Data
3.1 Min

5 Data Connectivity through Text Files
3.06 Min

6 Principles of Importing Data in Python
4.5 Min

7 More on Text Files (.txt vs .csv)
4.33 Min

8 Fixed-width Files
1.25 Min

9 Common Naming Conventions Used in Programming
3.49 Min

10 008 Data-Analyst-Common-Naming-Conventions.pdf
6 Min

11 Importing Text Files in Python ( open() )
9 Min

12 Importing Text Files in Python ( with open() )
4.53 Min

13 Importing .csv Files with pandas - Part I
5.35 Min

14 Importing .csv Files with pandas - Part II
2.37 Min

15 018 Lending-company.zip
7 Min

1 Importing .csv Files with pandas - Part III
5.57 Min

2 Importing Data with the index_col Parameter
2.35 Min

3 Importing Data with NumPy - .loadtxt() vs genfromtxt()
10.44 Min

4 Importing Data with NumPy - Partial Cleaning While Importing
7.22 Min

5 022 Lending-Company-Numeric-Data-NAN.zip
6 Min

1 Importing .json Files
5.15 Min

2 Prelude to Working with Excel Files in Python
3.4 Min

3 Working with Excel Data (the .xlsx Format)
1.55 Min

4 An Important Exercise on Importing Data in Python
5.44 Min

5 Importing Data with the pandas' Squeeze Method
3.23 Min

6 027 Importing-Text-Data-DA-Solution.zip
6 Min

1 A Note on Importing Files in Jupyter
3.1 Min

2 Saving Your Data with pandas
3.11 Min

3 Saving Your Data with NumPy - np.save()
5.24 Min

4 Saving Your Data with NumPy - np.savez()
5.12 Min

5 Saving Your Data with NumPy - np.savetxt()
3.59 Min

6 Working with Text Files - Conclusion
1 Min

7 036 Working-with-Text-Files-Lectures.zip
5 Min

1 Working with Text Data and Argument Specifiers
9.18 Min

2 Manipulating Python Strings
4.15 Min

3 Using Various Python String Methods - Part I
6.51 Min

4 Using Various Python String Methods - Part II
6.44 Min

5 String Accessors
4.5 Min

6 Using the .format() Method
9.02 Min

7 039 Working-with-Text-Data-Solutions.zip
8 Min

1 Triple Nested For Loops
5.38 Min

2 List Comprehensions
8.29 Min

3 Anonymous (Lambda) Functions
7 Min

4 017 Must-Know-Python-Tools-Exercises.zip
4 Min

5 Iterating Over Range Objects
4.18 Min

6 Nested For Loops - Introduction
6 Min

1 What is data gatheringdata collection
6.33 Min

1 Overview of APIs
3.1 Min

2 GET and POST Requests
2.35 Min

3 Data Exchange Format for APIs JSON
2.24 Min

4 Introducing the Exchange Rates API
4.58 Min

5 Including Parameters in a GET Request
3.18 Min

6 More Functionalities of the Exchange Rates API
4.39 Min

7 Coding a Simple Currency Conversion Calculator
4.52 Min

8 iTunes API
4.41 Min

9 iTunes API Structuring and Exporting the Data
2.11 Min

10 Pagination GitHub API
4.21 Min

11 004 APIs-complete-notebook.zip
6 Min

1 Data Cleaning and Data Preprocessing
5.26 Min

1 .unique(), .nunique()
3.49 Min

2 Converting Series into Arrays
3.29 Min

3 .sort_values()
3.57 Min

4 Attribute and Method Chaining
4.2 Min

5 .sort_index()
3.59 Min

6 022 pandas-Series-Solutions.zip
10 Min

1 A Revision to pandas DataFrames
5.06 Min

2 001 pandas-DataFrames.pdf
6 Min

3 Common Attributes for Working with DataFrames
4.16 Min

4 Data Selection in pandas DataFrames
6.56 Min

5 Data Selection - Indexing with .iloc[]
5.56 Min

6 Data Selection - Indexing with .loc[]
4.02 Min

7 001 Sales-products.zip
7 Min

1 006 pandas-DataFrames.pdf
5 Min

2 006 pandas-DataFrames-Exercises.zip
7 Min

1 Indexing in NumPy
5.52 Min

2 Assigning Values in NumPy
4.16 Min

3 Elementwise Properties of Arrays
4.3 Min

4 Types of Data Supported by NumPy
5.56 Min

5 Characteristics of NumPy Functions Part 1
4.43 Min

6 Characteristics of NumPy Functions Part 2
3.3 Min

7 007 NumPy-Fundamentals-Solution.zip
6 Min

1 Ndarrays
9.51 Min

2 Arrays vs Lists
6.55 Min

3 Strings vs Object vs Number
7.13 Min

1 Basic Slicing in NumPy
10.03 Min

2 Stepwise Slicing in NumPy
4.58 Min

3 Conditional Slicing in NumPy
4.52 Min

4 Dimensions and the Squeeze Function
6.52 Min

5 005 Working-With-Arrays-Solution.zip
8 Min

1 Arrays of 0s and 1s
5.32 Min

2 _like functions in NumPy
3.13 Min

3 A Non-Random Sequence of Numbers
5.02 Min

4 Random Generators and Seeds
5.22 Min

5 Basic Random Functions in NumPy
3.56 Min

6 Probability Distributions in NumPy
5.2 Min

7 Applications of Random Data in NumPy
4.09 Min

8 001 Generating-Data-With-NumPy-Complete.zip
9 Min

1 Using Statistical Functions in NumPy
7.44 Min

2 Minimal and Maximal Values in NumPy
6.02 Min

3 Statistical Order Functions in NumPy
6.25 Min

4 Averages and Variance in NumPy
4.17 Min

5 Covariance and Correlation in NumPy
2.59 Min

6 Histograms in NumPy (Part 1)
7.35 Min

7 Histograms in NumPy (Part 2)
4.15 Min

8 NAN Equivalent Functions in NumPy
3.08 Min

9 009 Statistics-With-NumPy-Exercise.zip
7 Min

1 Checking for Missing Values in Ndarrays
9.23 Min

2 Substituting Missing Values in Ndarrays
8.28 Min

3 Reshaping Ndarrays
6.31 Min

4 Removing Values from Ndarrays
4.2 Min

5 Sorting Ndarrays
9.46 Min

6 Argument Sort in NumPy
5.48 Min

7 001 Preprocessing-Data-With-NumPy-Template.zip
6 Min

1 Argument Where in NumPy
11.12 Min

2 Shuffling Ndarrays
6.52 Min

3 Casting Ndarrays
6.13 Min

4 Striping Values from Ndarrays
4.43 Min

5 Stacking Ndarrays
10.32 Min

6 Concatenating Ndarrays
6.27 Min

1 Finding Unique Values in Ndarrays
5.04 Min

2 013 Lending-company-Numeric-NAN.zip
6 Min

1 Setting Up Introduction to the Practical Example
4.5 Min

2 Setting Up Importing the Data Set
4.1 Min

3 Setting Up Checking for Incomplete Data
4.35 Min

4 Setting Up Splitting the Dataset
5.27 Min

5 Setting Up Creating Checkpoints
2.5 Min

6 Manipulating Text Data Issue Date
5.26 Min

7 Manipulating Text Data Loan Status and Term
7.08 Min

8 Manipulating Text Data Grade and Sub Grade
8.54 Min

9 Manipulating Text Data Verification Status & URL
5.2 Min

10 Manipulating Text Data State Address
6.02 Min

11 Manipulating Text Data Converting Strings and Creating a Checkpoint
3.28 Min

12 001 loan-data-dictionary
8 Min

1 Manipulating Numeric Data Substitute Filler Values
7.51 Min

2 Manipulating Numeric Data Currency Change – The Exchange Rate
6.32 Min

3 Manipulating Numeric Data Currency Change - From USD to EUR
8.22 Min

4 Completing the Dataset
6.46 Min

5 015 A-Loan-Data-Example-with-NumPy-Complete.zip
8 Min

1 An Introduction to the Absenteeism Exercise
1.11 Min

2 The Absenteeism Exercise from a Business Perspective
2.19 Min

3 The Dataset
1.34 Min

1 How to Complete the Absenteeism Exercise
1.57 Min

2 Eyeball Your Data First
5.53 Min

3 Note Programming vs the Rest of the World
1.27 Min

4 Using a Statistical Approach to Solve Our Exercise
2.17 Min

5 Dropping the 'ID' Column
6.27 Min

6 Analysis of the 'Reason for Absence' Column
5.4 Min

7 001 df-cleaned
4 Min

1 Splitting the Reasons for Absence into Multiple Dummy Variables
6.37 Min

2 Working with Dummy Variables - A Statistical Perspective
1.28 Min

3 Grouping the Reason for Absence Columns
8.35 Min

4 Concatenating Columns in a pandas DataFrame
4.37 Min

5 Reordering Columns in a DataFrame
1.43 Min

6 Working on the 'Date' Column
7.48 Min

1 Extracting the Month Value from the 'Date' Column
6.59 Min

2 Creating the 'Day of the Week' Column
3.36 Min

3 Understanding the Meaning of 5 More Columns
3.17 Min

4 Modifying the 'Education' Column
3.36 Min

5 Final Remarks on the Absenteeism Exercise
1.4 Min

6 data-cleaning-homework
2.4 Min

1 What Is Data Visualization and Why Is It Important
4.31 Min

2 Why Learn Data Visualization
6.8 Min

3 Choosing the Right Visualization – What Are Some Popular Approaches and Framewor
6.58 Min

4 Introduction into Colors and Color Theory
7.56 Min

5 Bar Chart - Introduction - General Theory and Getting to Know the Dataset
1.29 Min

6 Bar Chart - How to Create a Bar Chart Using Python
11.27 Min

7 Bar Chart – Interpreting the Bar Graph. How to Make a Good Bar Graph
1.5 Min

8 Pie Chart - Introduction - General Theory and Dataset
4.04 Min

9 Pie Chart - How to Create a Pie Chart Using Python
6.39 Min

10 Pie Chart – Interpreting the Pie Chart
1.32 Min

11 Data-Viz-Notebook-Template
4.56 Min

1 Pie Chart - Why You Should Never Create a Pie Graph
6.32 Min

2 Stacked Area Chart - Introduction - General Theory. Getting to Know the Dataset
3.16 Min

3 Stacked Area Chart - How to Create a Stacked Area Chart Using Python
7.47 Min

4 Stacked Area Chart - Interpreting the Stacked Area Graph
2.3 Min

5 Stacked Area Chart - How to Make a Good Stacked Area Chart
3.52 Min

6 Line Chart - Introduction - General Theory. Getting to Know the Dataset
8.05 Min

7 Line Chart - How to Create a Line Chart in Python
8.05 Min

8 Line Chart - Interpretation
3.11 Min

9 Line Chart - How to Make a Good Line Chart
6.3 Min

10 istogram - Introduction - General Theory. Getting to Know the Dataset
4.39 Min

11 Histogram - How to Create a Histogram Using Python
5.43 Min

12 Histogram – Interpreting the Histogram
2.11 Min

1 Histogram – Choosing the Number of Bins in a Histogram
5.27 Min

2 Histogram - How to Make a Good Histogram
4.43 Min

3 Scatter Plot - Introduction - General Theory. Getting to Know the Dataset
2.29 Min

4 Scatter Plot - How to Create a Scatter Plot Using Python
8.39 Min

5 Scatter Plot – Interpreting the Scatter Plot
2.42 Min

6 Scatter Plot - How to Make a Good Scatter Plot
2.56 Min

7 Regression Plot - Introduction - General Theory. Getting to Know the Dataset
3.03 Min

8 Regression Plot - How to Create a Regression Scatter Plot Using Python
7.08 Min

9 Regression Plot – Interpreting the Regression Scatter Plot
4.35 Min

10 Regression Plot - How to Make a Good Regression Plot
3.14 Min

11 Bar and Line Chart - Introduction - General Theory. Getting to Know the Dataset
3.1 Min

12 Bar and Line Chart - How to Create a Combination Bar and Line Graph Using Python
7.39 Min

13 Bar and Line Chart – Interpreting the Combination Bar and Line Graph
2.36 Min

14 Bar and Line Chart – How to Make a Good Bar and Line Graph
4.04 Min

15 037 Data-Viz-Homework-Solution-Notebook
6.89 Min

1 Conclusion
2.22 Min

Instructor

Smrutirupa Parida

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  • Duration 94 Hours 4 Min

  • Lectures 297 lessons

  • Enrolled 72 students

  • Category: DATA

  • 0 Quizzes

  • Language: English

  • Skill Level Beginner

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