Zaalima Learning LMS - Zaalima Development | Data Analyst Complete Course

Course / Course Details

Data Analyst Complete Course

Data

By - Zaalima Learning
0 students
38 Hours
(0)

Course Requirements

Basic computer knowledge
No prior coding experience required
Laptop/Desktop with internet connection
Basic understanding of mathematics (helpful but not mandatory)

Course Description

The Data Analyst Complete Course is designed to equip learners with the essential skills required to analyze, interpret, and visualize data effectively. This program covers Excel, SQL, Python, Statistics, Data Visualization, and Power BI/Tableau to help students make data-driven decisions.

Through hands-on projects, real-world datasets, and practical case studies, learners will gain strong analytical skills and industry-ready experience.

Course Outcomes

Understand data analysis fundamentals
Work confidently with Excel for data cleaning and reporting
Write SQL queries to extract and manage data
Analyze data using Python (Pandas, NumPy, Matplotlib)
Perform statistical analysis for decision-making
Create interactive dashboards using Power BI or Tableau
Clean and preprocess real-world datasets
Build portfolio-ready data analytics projects
Prepare for Data Analyst job interviews

Course Curriculum

27 chapters
260 lectures
0 quizzes
38 Hours total length

Toggle all chapters

1 A Practical Example - What Will You Learn in This Course

5 Min

2 What Does the Course Cover

6 Min

1 Introduction to the World of Business and Data

3 Min

2 Relevant Terms Explained

6 Min

3 Data Analyst Compared to Other Data Jobs

3 Min

4 Data Analyst Job Description

6 Min

5 Why Python

6 Min

1 Introduction

2 Min

2 Programming Explained in a Few Minutes

6 Min

3 Jupyter - Introduction

4 Min

4 Jupyter - Installing Anaconda

4 Min

5 Jupyter - Intro to Using Jupyter

4 Min

6 Jupyter - Working with Notebook Files

7 Min

7 Jupyter - Using Shortcuts

4 Min

8 Jupyter - Handling Error Messages

6 Min

9 Jupyter - Restarting the Kernel

3 Min

1 Sequences - List Slicing

5 Min

2 Sequences - Tuples

4 Min

3 Sequences - Dictionaries

5 Min

4 Iteration - While Loops and Incrementing

3 Min

5 Sequences - List Slicing

6 Min

6 Iteration - For Loops

3 Min

7 Iteration - While Loops and Incrementing

3 Min

8 Iteration - Create Lists with the range() Function

3 Min

9 Iteration - Use Conditional Statements and Loops Together

3 Min

10 Iteration - Conditional Statements, Functions, and Loops

2 Min

11 Iteration - Iterating over Dictionaries

4 Min

12 Python Variables

4 Min

13 Types of Data - Numbers and Boolean Values

4 Min

14 Types of Data - Strings

6 Min

15 Basic Python Syntax - Arithmetic Operators

4 Min

16 Basic Python Syntax - Reassign Values

1 Min

17 Basic Python Syntax - Add Comments

2 Min

18 Basic Python Syntax - Line Continuation

2 Min

19 Basic Python Syntax - Indexing Elements

2 Min

20 Basic Python Syntax - Indentation

2 Min

21 Operators - Comparison Operators

3 Min

22 Operators - Logical and Identity Operators

6 Min

23 Conditional Statements - The IF Statement

3 Min

24 Conditional Statements - The ELSE Statement

3 Min

25 Conditional Statements - The ELIF Statement

6 Min

26 Conditional Statements - A Note on Boolean Values

2 Min

27 Functions - Defining a Function in Python

2 Min

28 Functions - Creating a Function with a Parameter

4 Min

29 Functions - Another Way to Define a Function

3 Min

30 Functions - Using a Function in Another Function

2 Min

31 Functions - Combining Conditional Statements and Functions

3 Min

32 Functions - Creating Functions That Contain a Few Arguments

2 Min

33 Functions - Notable Built-in Functions in Python

4 Min

34 Sequences - Lists

4 Min

35 Sequences - Using Methods

4 Min

1 Object-Oriented Programming (OOP)

5 Min

2 Modules, Packages, and the Python Standard Library

5 Min

3 Importing Modules

4 Min

4 Introduction to Using NumPy and pandas

10 Min

5 What is Software Documentation

6 Min

6 The Python Documentation

7 Min

1 What Is а Matrix

4 Min

2 Scalars and Vectors

3 Min

3 Linear Algebra and Geometry

3.1 Min

4 Arrays in Python

5.3 Min

5 What Is a Tensor

3 Min

6 Adding and Subtracting Matrices

4 Min

7 Errors When Adding Matrices

2 Min

8 Transpose

5.3 Min

9 Dot Product of Vectors

4 Min

10 Dot Product of Matrices

9 Min

11 Why is Linear Algebra Useful

10.1 Min

1 The NumPy Package and Why We Use It

4.3 Min

2 InstallingUpgrading NumPy

2 Min

3 Ndarray

3.3 Min

4 The NumPy Documentation

5 Min

1 Introduction to the pandas Library

6 Min

2 Installing and Running pandas

6 Min

3 Introduction to pandas Series

9 Min

4 Working with Attributes in Python

6 Min

5 Using an Index in pandas

4 Min

6 Label-based vs Position-based Indexing

5 Min

7 More on Working with Indices in Python

6 Min

8 Using Methods in Python - Part I

5 Min

9 Using Methods in Python - Part II

3 Min

10 Parameters vs Arguments

5 Min

11 The pandas Documentation

10 Min

12 Introduction to pandas DataFrames

6 Min

13 Creating DataFrames from Scratch - Part I

6 Min

14 Creating DataFrames from Scratch - Part II

5 Min

15 Additional Notes on Using DataFrames

2 Min

1 Working with Files in Python - An Introduction

4 Min

2 File vs File Object, Read vs Parse

3 Min

3 Structured vs Semi-Structured and Unstructured Data

3 Min

4 Data Connectivity through Text Files

3 Min

5 Principles of Importing Data in Python

3 Min

6 More on Text Files (.txt vs .csv)

5 Min

7 Fixed-width Files

2 Min

8 Common Naming Conventions Used in Programming

4 Min

9 Importing Text Files in Python ( open() )

9 Min

10 Importing Text Files in Python ( with open() )

5 Min

11 Importing .csv Files with pandas - Part I

6 Min

12 Importing .csv Files with pandas - Part II

3 Min

13 Importing .csv Files with pandas - Part III

6 Min

14 Importing Data with the index_col Parameter

3 Min

15 Importing Data with NumPy - .loadtxt() vs genfromtxt()

11 Min

16 Importing Data with NumPy - Partial Cleaning While Importing

8 Min

17 Importing .json Files

6 Min

18 Prelude to Working with Excel Files in Python

4 Min

19 Working with Excel Data (the .xlsx Format)

2 Min

20 An Important Exercise on Importing Data in Python

6 Min

21 Importing Data with the pandas' Squeeze Method

4 Min

22 A Note on Importing Files in Jupyter

4 Min

23 Saving Your Data with pandas

3 Min

24 Saving Your Data with NumPy - np.save()

6 Min

25 Saving Your Data with NumPy - np.savez()

6 Min

26 Saving Your Data with NumPy - np.savetxt()

4 Min

27 Working with Text Files - Conclusion

1 Min

1 Working with Text Data and Argument Specifiers

10 Min

2 Manipulating Python Strings

5 Min

3 Using Various Python String Methods - Part I

7 Min

4 Using Various Python String Methods - Part II

7 Min

5 String Accessors

5 Min

6 Using the .format() Method

10 Min

1 Iterating Over Range Objects

5 Min

2 Nested For Loops - Introduction

6 Min

3 Triple Nested For Loops

6 Min

4 List Comprehensions

9 Min

5 Anonymous (Lambda) Functions

7 Min

1 What is data gatheringdata collection

7 Min

1 Overview of APIs

4 Min

2 GET and POST Requests

3 Min

3 Data Exchange Format for APIs JSON

3 Min

4 Introducing the Exchange Rates API

5 Min

5 Including Parameters in a GET Request

4 Min

6 More Functionalities of the Exchange Rates API

5 Min

7 Coding a Simple Currency Conversion Calculator

5 Min

8 iTunes API

5 Min

9 iTunes API Structuring and Exporting the Data

3 Min

10 Pagination GitHub API

5 Min

1 Data Cleaning and Data Preprocessing

6 Min

1 Unique(), .nunique()

4 Min

2 Converting Series into Arrays

6 Min

3 sort_values()

4 Min

4 Attribute and Method Chaining

5 Min

5 Sort_index()

4 Min

6 Characteristics of NumPy Functions Part 2

4 Min

1 A Revision to pandas DataFrames

5 Min

2 Common Attributes for Working with DataFrames

5 Min

3 Data Selection in pandas DataFrames

7 Min

4 Data Selection - Indexing with .iloc[]

6 Min

5 Data Selection - Indexing with .loc[]

4 Min

1 Indexing in NumPy

6 Min

2 Assigning Values in NumPy

5 Min

3 Elementwise Properties of Arrays

5 Min

4 Types of Data Supported by NumPy

6 Min

5 Characteristics of NumPy Functions Part 1

5 Min

1 Ndarrays

10 Min

2 Arrays vs Lists

7 Min

3 Strings vs Object vs Number

8 Min

1 Basic Slicing in NumPy

10 Min

2 Stepwise Slicing in NumPy

5 Min

3 Conditional Slicing in NumPy

5 Min

4 Dimensions and the Squeeze Function

7 Min

1 Arrays of 0s and 1s

6 Min

2 like functions in NumPy

4 Min

3 A Non-Random Sequence of Numbers

5 Min

4 Random Generators and Seeds

6 Min

5 Basic Random Functions in NumPy

4 Min

6 Probability Distributions in NumPy

6 Min

7 Applications of Random Data in NumPy

4 Min

1 Using Statistical Functions in NumPy

8 Min

2 Minimal and Maximal Values in NumPy

6 Min

3 Statistical Order Functions in NumPy

7 Min

4 Averages and Variance in NumPy

5 Min

5 Covariance and Correlation in NumPy

3 Min

6 Histograms in NumPy (Part 1)

8 Min

7 Histograms in NumPy (Part 2)

5 Min

8 NAN Equivalent Functions in NumPy

4 Min

1 Checking for Missing Values in Ndarrays

10 Min

2 Substituting Missing Values in Ndarrays

9 Min

3 Reshaping Ndarrays

7 Min

4 Removing Values from Ndarrays

5 Min

5 Sorting Ndarrays

10 Min

6 Argument Sort in NumPy

6 Min

7 Argument Where in NumPy

12 Min

8 Shuffling Ndarrays

7 Min

9 Casting Ndarrays

7 Min

10 Striping Values from Ndarrays

5 Min

11 Stacking Ndarrays

11 Min

12 Concatenating Ndarrays

7 Min

13 Finding Unique Values in Ndarrays

6 Min

1 Setting Up Introduction to the Practical Example

5 Min

2 Setting Up Importing the Data Set

5 Min

3 Setting Up Checking for Incomplete Data

5 Min

4 Setting Up Splitting the Dataset

6 Min

5 Setting Up Creating Checkpoints

3 Min

6 Manipulating Text Data Issue Date

6 Min

7 Manipulating Text Data Loan Status and Term

7 Min

8 Manipulating Text Data Grade and Sub Grade

9 Min

9 Manipulating Text Data Verification Status & URL

6 Min

10 Manipulating Text Data State Address

6 Min

11 Manipulating Text Data Converting Strings and Creating a Checkpoint

4 Min

12 Manipulating Numeric Data Substitute Filler Values

8 Min

13 Manipulating Numeric Data Currency Change – The Exchange Rate

7 Min

14 Manipulating Numeric Data Currency Change - From USD to EUR

9 Min

15 Completing the Dataset

7 Min

1 An Introduction to the Absenteeism Exercise

2 Min

2 The Absenteeism Exercise from a Business Perspective

3 Min

3 The Dataset

2 Min

1 How to Complete the Absenteeism Exercise

2 Min

2 Eyeball Your Data First

6 Min

3 Note Programming vs the Rest of the World

4 Min

4 Using a Statistical Approach to Solve Our Exercise

3 Min

5 Dropping the 'ID' Column

7 Min

6 Analysis of the 'Reason for Absence' Column

5 Min

7 Splitting the Reasons for Absence into Multiple Dummy Variables

9 Min

8 Working with Dummy Variables - A Statistical Perspective

2 Min

9 Grouping the Reason for Absence Columns

9 Min

10 Concatenating Columns in a pandas DataFrame

5 Min

11 Reordering Columns in a DataFrame

2 Min

12 Working on the 'Date' Column

8 Min

13 Extracting the Month Value from the 'Date' Column

7 Min

14 Creating the 'Day of the Week' Column

14 Min

15 Understanding the Meaning of 5 More Columns

4 Min

16 Modifying the 'Education' Column

5 Min

17 Final Remarks on the Absenteeism Exercise

2 Min

1 What Is Data Visualization and Why Is It Important

5 Min

2 Why Learn Data Visualization

7 Min

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

7 Min

4 Introduction into Colors and Color Theory

9 Min

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

2 Min

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

12 Min

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

3 Min

8 Pie Chart - Introduction - General Theory and Dataset

4 Min

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

7 Min

10 Pie Chart – Interpreting the Pie Chart

2 Min

11 Pie Chart - Why You Should Never Create a Pie Graph

8 Min

12 Stacked Area Chart - Introduction - General Theory. Getting to Know the Dataset

4 Min

13 Stacked Area Chart - How to Create a Stacked Area Chart Using Python

8 Min

14 Stacked Area Chart - Interpreting the Stacked Area Graph

3 Min

15 Stacked Area Chart - How to Make a Good Stacked Area Chart

4 Min

16 Line Chart - Introduction - General Theory. Getting to Know the Dataset

2 Min

17 Line Chart - How to Create a Line Chart in Python

8 Min

18 Line Chart - Interpretation

4 Min

19 Line Chart - How to Make a Good Line Chart

7 Min

20 Histogram - Introduction - General Theory. Getting to Know the Dataset

5 Min

21 Histogram - How to Create a Histogram Using Python

6 Min

22 Histogram – Interpreting the Histogram

3 Min

23 Histogram – Choosing the Number of Bins in a Histogram

6 Min

24 Histogram - How to Make a Good Histogram

5 Min

25 Scatter Plot - Introduction - General Theory. Getting to Know the Dataset

3 Min

26 Scatter Plot - How to Create a Scatter Plot Using Python

9 Min

27 Scatter Plot – Interpreting the Scatter Plot

3 Min

28 Scatter Plot - How to Make a Good Scatter Plot

3 Min

29 Regression Plot - Introduction - General Theory. Getting to Know the Dataset

3 Min

30 Regression Plot - How to Create a Regression Scatter Plot Using Python

7 Min

31 Regression Plot – Interpreting the Regression Scatter Plot

5 Min

32 Regression Plot - How to Make a Good Regression Plot

4 Min

33 Bar and Line Chart - Introduction - General Theory. Getting to Know the Dataset

4 Min

34 Bar and Line Chart - How to Create a Combination Bar and Line Graph Using Python

8 Min

35 Bar and Line Chart – Interpreting the Combination Bar and Line Graph

3 Min

36 Bar and Line Chart – How to Make a Good Bar and Line Graph

4 Min

1 Conclusion

3 Min

Instructor

Zaalima Learning

As the Super Admin of our platform, I bring over a decade of experience in managing and leading digital transformation initiatives. My journey began in the tech industry as a developer, and I have since evolved into a strategic leader with a focus on innovation and operational excellence. I am passionate about leveraging technology to solve complex problems and drive organizational growth. Outside of work, I enjoy mentoring aspiring tech professionals and staying updated with the latest industry trends.

5 Rating