Data Analytics Zero to Hero: 5 Experts from FAANG

Start Your Data Journey: Master Excel, Python, SQL, and Data Visualization w/Tableau

Created byH MetaCode

Last updated 11/2025

English

What you'll learn

Analyze e-commerce funnels, interpret financial trends, optimize marketing performance, and apply FAANG-level product analytics frameworks
Master the Fundamentals of Excel for Data Work
Understand and Apply Python Basics for Data Analysis
Build a Strong Foundation in SQL for Querying Databases
Integrate Python and SQL for End-to-End Data Analysis
Apply Skills to Real-World Data Projects

Course content

6 sections • 160 lectures • 40h 23m total length

0. Introduction1:55
1-1. Introduction to Excel Interface2:50
1-2. Basic Navigation and Shortcuts7:06
1-3. Cell Formatting9:40
1-4. Data Entry, auto-Fill, and Basic Calculations8:12
1-5. Practice9:04
2-1. Overview of Basic Formulas3:23
2-2. Logical Functions8:10
2-3. Lookup Functions13:02
2-4. Text Functions13:33
2-5. Date and Time Functions12:15
2-6. Error-handiling Functions11:06
2-7. Practice17:01
3-1. Working with Tables11:33
3-2. Conditional Formatting8:05
3-3. Sorting and Filtering Data13:48
3-4. Removing Duplicates and Handling Missing Data13:17
3-5. Practice16:10
4-1. Creating and Customizing Charts14:11
4-2. Advanced Charts16:21
4-3. Best Practice for Visualizing Data for Business Analysis10:32
4-4. Practice18:26
5-1. Creating and Customizing Pivot Tables14:19
5-2. Grouping, Sorting and Filtering Pivot Data12:56
5-3. Calculated Fields and Items0:55
5-4. Advanced Pivot Table Techniques10:29
5-5. Pivot Charts for Dynamic Visualization10:14
5-6. Practice18:49
6-1. Recording and Running Macros9:46
6-2. Editing VBA Code for Custom Automation14:10
6-3. Debugging and Troubleshooting Macros11:16
6-4. Writing Simple VBA Scripts24:58
7-1. Data Cleaning and Preparation30:53
7-2. Applying Formulas and Analysis Techniques with Pivot Tables12:28
7-3. Visualizing Insights with Charrs and Pivot Tables25:57

0. Introduction0:58
1-1. Why Python2:04
1-2. Setting Up Python with Visual Studio Code5:30
1-3-1. Printing and Variable (Theory)8:28
1-3-2. Printing and Variables (Practice)20:17
2-1-1. Numbers (Theory)3:41
2-1-2. Numbers (Practice)25:57
2-2-1. Booleans (Theory)2:54
2-2-2. Booleans (Practice)12:05
2-3-1. Strings (Theory)6:52
2-3-2. Strings (Practice)25:02
3-1-1. Lists14:41
3-1-2. Lists (Practice)33:17
3-2-1. Dictionaries (Theory)8:02
3-2-2. Dictionaries (Practice)12:50
3-3-1. Tuples and Sets9:21
3-3-2. Tuples and Sets (Practice)12:19
4-1-1. if Statements (Theory)7:35
4-1-2. if Statements (Practice)28:41
4-2-1. for Loops (Theory)9:21
4-2-2. for Loops (Practice)22:01
4-3-1. while Loops (Theory)5:04
4-3-2. while Loops (Practice)13:00
5-1-1. Reading from Files5:59
5-1-2. Reading from Files26:22
5-2-1. Writing to Files3:44
5-2-2. Writing to files (Practice)19:32
6-1-1. Functions (Theory)10:14
6-1-2. Functions (Practice)21:47
6-2-1. Classes (Theory)9:25
6-2-2. Classes (Practice)24:12
7-1-1. Numpy (Theory)13:33
7-1-2. Numpy (Practice)29:25
7-2-1. Matplotlib (Theory)14:02
7-2-2. Matplotlib (Practice)27:45
7-3-1. Pandas (Theory)26:47
7-3-2. Pandas (Practice)32:58
8-1. Final Deep Dive (Introducing Project)4:30
8-2. Final Deep Dive (Dataset)5:48
8-3. Final Deep Dive (Project part 1)24:45
8-4. Final Deep Dive (Project part 2)24:17
8-5. Final Deep Dive (Project part 3)17:08
8-6. Final Deep Dive (Project part 4)17:04

1-1. SQL Introduction10:47
1-2. SQL Functions (DDL, DML, DCL, DQL, Datatypes)12:14
1-3. Download SQL10:06
1-4. Setting SQL Environment18:39
2-1. SELECT&FROM18:34
2-2. JOIN12:22
2-3. Chapter 2 Quiz21:58
3-1. ORDERBY, GROUPBY17:35
3-2. WHERE&HAVING22:33
4-1. AND, OR, BETWEEN15:48
4-2. LIKE, IN, IS NULL26:15
4-3. NULL Value28:48
5-1. Data Aggregation(SUM, AVG, MIN, MAX, COUNT)16:56
5-2. Chapter 5 quiz18:36
6-1. Final Project Part 128:32
6-2. Final Project Part 231:31

0. Intro (Project Overview)3:03
1. Importing Data using Pandas Library6:52
2-1. Exploring Datasets18:00
2-2. Data Cleaning and Aggregation - Part 117:07
2-3. Data Cleaning and Aggregation - Part 235:06
3-1. Data Visualization (Theory)17:50
3-2. Data Visualization (Practice)22:04
3-3. Correlation Matrix Heatmap27:56
4-1. Statistical Testing (Theory)3:02
4-2. Statistical Testing (Practice)29:18
5-1. Conclusion&Recommendation8:10
6-1. Olist E-Commerce Analytics Introduction12:13
6-2. Build Tables12:30
6-3. Exploring Table and Building Understanding13:14
6-4. Question 143:33
6-5. Question 236:08
6-6. Question 340:06
7-1. Amazon Top 50 Best Selling Books Introduction4:56
7-2. Data Overview26:33
7-3. Question 19:36
7-4. Question 2-34:53
7-5. Question 4-613:50

1-1. Introduction to Tableau and Installation4:48
1-2. Getting Started with Tableau Public10:02
1-3. Tables, Bar, and Line Charts12:56
1-4. Histograms and Filters13:27
1-5. Building Your First Dashboard24:49
2-1. Dual-Axis and Combo Charts10:42
2-2. Maps and Geographic Data Visualizations13:42
2-3. Highlight Tables, Heatmaps, Tree Maps, and Bullet Charts33:58
2-4. Introduction to Level of Detail Expressions16:29
2-5. Parameters and Filters - Dynamic Dashboard18:44
3-1. Building Students Metrics Dashboards15:47
3-2. Visualizing Time Trends6:29
3-3. Case Study- Profit Analysis11:01
3-4. Storytelling- Presenting Insights9:26
3-5. Combining Data- Blending vs Joining13:55
4-1-1. Exploring the Dataset and Defining the Problem4:45
4-1-2. Exploring the Dataset and Defining the Problem1:56
4-2. Designing the Dashboard3:38
4-3. Building the Dashboard23:14
4-4. Insights and Storytelling6:40
4-5. Presenting and Refining the Dashboard3:29

0. Introduction5:34
1-1. What is Statistics2:58
1-2. Application of Statistics in Data Analysis7:08
1-3. Types of Data- Qualitative vs Quantitative11:16
1-4. Key Concepts in Statistics11:47
2-1. Measures of Central Tendency(Mean, Median, Mode)7:28
2-2. Measures of Dispersion(Range, Variance, Std Dev, IQR)5:44
2-3. Data Visualization Techniques(Histograms, Boxplots, Scatterplots)40:09
3-1. Basics of Probability(Rules, Joint, Conditional, Independent Events)14:57
3-2. Introduction to Probability Distributions(Normal, Binomial, Poisson)11:31
3-3. Sampling Methods & The Central Limit Theorem10:28
4-1. Hypothesis Testing Basics(Null vs Alternative, P-values)16:37
4-2. Common Hypothesis Tests(t-Test, ANOVA)11:43
4-3. Confidence Intervals & Estimation23:34
5-1. Understanding Correlation(Pearson, Spearman)15:30
5-2. Simple Linear Regression15:15
5-3. Multiple Linear Regression(Multicollinearity, Interpretation)25:35
6-1. Introduction to Time Series Data10:24
6-2. Trend, Seasonality, and Noise11:48
6-3. Basic Forecasting Methods11:55
6-4. Advanced Forecasting Models21:03
7-1. Real-World Data Challenges (Dealing with Missing Data, Outliers)54:01
7-2. Hands-on Project (EDA & Regression)13:27

Requirements

No prerequisites required for this course.

Description

Start Your Data Journey: Master Excel, Python, SQL, and Full Pipeline Integration

Embark on your data career with Data Analytics Zero to Hero, a beginner-friendly yet highly practical program designed to transform absolute beginners into confident, capable data practitioners. This course provides a complete foundation in the three essential tools of modern analytics—Excel, Python, and SQL—and guides you toward building a full end-to-end analysis pipeline that mirrors real industry workflows.

Your journey starts with Excel analytics, where you develop a strong grasp of spreadsheets, formulas, data cleaning techniques, and essential visualizations. This foundation not only strengthens analytical thinking but also prepares you for more advanced tooling later in the course.

From there, you transition into Python, one of the most powerful and widely used languages in the data world. Through clear, step-by-step lessons, you will write basic Python scripts, work with key libraries like Pandas and Matplotlib, clean and transform datasets, and create visual insights—skills used daily by real data analysts.

Next, you dive into SQL, the backbone of data storage and retrieval in organizations. You’ll learn how to query relational databases, filter and transform data, join multiple tables, and perform aggregations to uncover meaningful insights from structured data.

In the final phase, the course connects everything together through SQL-Python integration. You will learn how to pull data from SQL directly into Python, automate workflows, and combine both tools to build a seamless, end-to-end analytics pipeline—just like a professional data analyst.

By the end of this program, you will have the technical foundation, practical experience, and confidence to perform real analytical tasks. Data Analytics Zero to Hero is the perfect starting point for anyone seeking to break into data roles, improve workplace performance, or prepare for more advanced analytics and machine learning studies.

Who this course is for:

Anyone who wants to improve their data analysis skills

Data Analytics Zero to Hero: 5 Experts from FAANG

What you'll learn

Explore related topics

Course content

Introduction to Excel Analytics35 lectures • 7hr 17min

Introduction to Python43 lectures • 10hr 49min

Introduction to SQL16 lectures • 5hr 11min

Integration of SQL and Python for Data Analytics22 lectures • 6hr 46min

Tableau Basics & Intermediate21 lectures • 4hr 20min

Introduction to Statistics23 lectures • 6hr

Requirements

Description

Who this course is for: