Learn Sql for data science Online: Free Course with AI Tutor & Certificate
Companies struggle to make sense of massive datasets. This free Sql for data science course teaches you how to extract and analyze that information. Over 34 hours and 197 lessons, you'll learn to filter records, spot trends, and build real dashboards to drive business decisions.
What you'll learn in this Sql for data science course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Understand the fundamentals of data analytics and its importance in business strategy.
Perform data collection and data wrangling using various techniques and tools.
Conduct exploratory data analysis to identify trends, patterns, and outliers in data.
Apply statistical analysis techniques to make data-driven decisions.
Use data visualization techniques to effectively communicate insights from data.
Sql for data science course syllabus
01 Introduction to Data Analytics
- Understanding Data Analytics
- Why You Should Learn Data Analytics?
- Is Data Analysis a Good Career Choice?
- Essential Data Analytics Trends to Watch
- Five Key Skills to Become an Exceptional Data Analyst
- The Critical Role of a Data Analyst
- The Importance of Data in Business Strategy
02 Basics of Data Analytics
- Different Types of Data Analytics
- The Data Analytics Lifecycle Explained
- Understanding Qualitative and Quantitative Data Types
- Exploring Various Data Sources
- The Importance of Data Quality
- An Introduction to Data Warehousing
- Fundamentals of Data Mining
03 Data Collection and Data Wrangling
- An Overview of Data Collection Methods
- Survey Data Collection Techniques
- Observation Data Collection Methods
- Focus Group Data Collection Strategies
- Interview Data Collection Techniques
- An Introduction to Web Scraping
- Using APIs for Data Collection
- An Overview of Data Wrangling
- Key Data Transformation Techniques
- Normalization and Standardization Methods
- Effective Data Parsing Techniques
04 Exploratory Data Analysis (EDA)
- What is Exploratory Data Analysis?
- Understanding Descriptive Statistics
- Univariate, Bivariate, and Multivariate Analysis Explained
- The Basics of Dimensionality Reduction
- Identifying Trends and Patterns in Data
- Conducting Outlier Analysis
- Exploratory Data Analysis with Python and R: Part 1
- Exploratory Data Analysis with Python and R: Part 2
05 Statistical Analysis
- Introduction to Statistical Analysis
- Basics of Probability Theory
- Sampling Methods and Techniques
- Understanding Hypothesis Testing
- Calculating Confidence Intervals
- Conducting T-tests and ANOVA
- Fundamentals of Regression Analysis
- Performing Chi-Square Tests
- Techniques for Correlation Analysis
- Correlation vs. Causation Explained
- Basics of Time Series Analysis
06 Data Visualization Techniques
- Introduction to Data Visualization
- Data Visualization Techniques: Part 1
- Data Visualization Techniques: Part 2
- Creating Charts in Excel: Part 1
- Creating Charts in Excel: Part 2
- Building Interactive Dashboards in Tableau
- Using Power BI for Data Visualization: Part 1
- Using Power BI for Data Visualization: Part 2
- Creating Visualizations in Python
- D3 for Data Visualization: Part 1
- D3 for Data Visualization: Part 2
- Mastering Storytelling with Data
07 Machine Learning Basics
- Introduction to Machine Learning
- Understanding Supervised vs. Unsupervised Learning
- Overview of Common Machine Learning Algorithms
- Preparing Data for Machine Learning
- Techniques for Feature Selection and Engineering
- Model Training and Evaluation Explained
- Addressing Overfitting and Underfitting
- Cross-Validation Techniques for Model Assessment
- Introduction to Scikit-Learn
- Implementing Linear Regression
- Implementing Logistic Regression
- Understanding Decision Trees
- An Overview of Random Forests
08 Predictive Analytics
- What is Predictive Analytics?
- Understanding Predictive Modeling
- Building Predictive Models: Part 1
- Building Predictive Models: Part 2
- ARIMA and SARIMA Models: Part 1
- ARIMA and SARIMA Models: Part 2
- Ensemble Methods: Bagging and Boosting
09 Big Data Analytics
- Introduction to Big Data
- Understanding the 6 V's of Big Data
- Big Data Technologies: Hadoop and Spark
- Introduction to NoSQL Databases
- Big Data Processing Frameworks Explained
- Understanding HDFS and MapReduce
- Implementing Spark for Big Data Analytics
- Real-Time Data Processing with Kafka
- Applications of Big Data
10 Excel for Data Analytics
- Data Cleaning Techniques in Excel
- Mastering Advanced Pivot Tables
- Using Excel for Statistical Analysis
- Conditional Formatting and Data Bars in Excel
- Automating with Excel Macros
- Using Power Query for Data Transformation
- Using Excel for Financial Modeling
- Excel Tips and Tricks for Data Analysts
11 Python for Data Analytics
- Introduction to Python for Data Analytics
- Understanding Variables in Python
- Working with Lists in Python
- Using Tuples and Strings in Python
- Dictionaries and Sets in Python
- Basic Math Operations in Python for Data Analysis
- Understanding Basic Data Types in Python
- Control Flow in Python: if, else, for, while
12 Pandas Basics for Data Analytics
- What is Pandas in Python?
- Basics of DataFrames
- Different Ways to Create a DataFrame
- Reading and Writing Data in Pandas
- Understanding Pandas DataFrames
13 NumPy Essentials for Data Analytics
- Introduction to NumPy
- NumPy Functions Tutorial: np.ones, np.zeros, np.empty
- NumPy Functions: np.arange(), np.flatten(), np.ravel()
- Slicing Operations in NumPy Arrays
- Performing Mathematical Operations in NumPy
- Trigonometric Operations in NumPy
- Random Operations in NumPy
- String Operations in NumPy
14 Data Manipulation Using Pandas
- Working with Lists and Dictionaries in Pandas
- Pandas DataFrame and Reading CSV Files
- Handling Null Values in Pandas
- Handling Missing Data: fillna, dropna, interpolate
- Handling Missing Data: replace Function
- Group By (Split Apply Combine) in Pandas
- Concatenating DataFrames in Pandas
- Merging DataFrames in Pandas
- Reshaping DataFrames Using Melt
- Stack and Unstack Operations in Pandas
- Using Crosstab in Pandas
- Handling Large Datasets in Pandas
15 Data Cleaning and Preparation with Python
- Exploring and Cleaning Data in Pandas
- Working with Text Data in Pandas
- Dealing with Dates in Pandas
- Joining Data in Pandas
- Creating Frequency Tables in Pandas
16 R for Data Analytics
- Understanding Variables in R
- Working with Vectors and Lists in R
- Creating and Using Matrices in R
- Understanding Arrays in R
- Working with Data Frames in R
- Understanding Factors in R
- Basic Math Operations in R
- Understanding Data Types in R
- Using Control Structures in R
- Defining and Using Functions in R
17 Data Manipulation with dplyr in R
- Introduction to dplyr in R
- Selecting Columns with select() in dplyr
- Filtering Rows with filter() in dplyr
- Arranging Rows with arrange() in dplyr
- Mutating Columns with mutate() in dplyr
- Summarizing Data with summarize() in dplyr
- Group By (Split Apply Combine) in dplyr
- Joining Data Frames with join() in dplyr
18 Data Cleaning and Preparation with R
- Exploring Data in R: Part 1
- Exploring Data in R: Part 2
- Cleaning Data in R: Part 1
- Cleaning Data in R: Part 2
- Working with Text Data in R: Part 1
- Working with Text Data in R: Part 2
- Preparing Numeric Data in R
- Dealing with Dates in R
- Handling Missing Data in R
- Handling Duplicates in R
- Converting Data Types in R
19 Machine Learning with R for Data Analytics
- Introduction to Machine Learning with R
- Building Linear Models in R
- Creating Decision Trees in R
- Using Random Forests in R
- Implementing Support Vector Machines in R
20 Working with Large Datasets in R
- Introduction to Data.table in R: Part 1
- Introduction to Data.table in R: Part 2
- Handling Large Datasets in R
- Efficient Data Manipulation with Data.table
- Connecting to Databases from R
- Reading and Writing Data in R
21 SQL for Data Analytics
- Introduction to SQL for Data Analytics
- Setting Up Databases, Tables, and Values
- Writing SELECT Statements in SQL
- Using the WHERE Clause in SQL
- Sorting Data with ORDER BY in SQL
- Using JOINs in SQL
- Using UNION and UNION ALL in SQL
- Applying Aggregate Functions in SQL
- Grouping Data with GROUP BY in SQL
- Writing Subqueries in SQL
- Understanding SQL Window Functions
- SQL Query Optimization Techniques
22 Data Manipulation and Transformation with SQL
- Approaching Complex SQL Queries
- Inserting Data with INSERT INTO
- Updating Data with UPDATE
- Deleting Data with DELETE
- Working with Dates and Times in SQL
23 Data Cleaning in SQL
- Data Cleaning in SQL: Part 1
- Data Cleaning in SQL: Part 2
- Data Cleaning in SQL: Part 3
- Data Cleaning in SQL: Part 4
- Data Cleaning in SQL: Part 5
24 Data Analytics Tools
- The Nine Essential Tools for Data Analytics
- Overview of Google Analytics
- Introduction to Tableau
- Introduction to Power BI
Real-world projects you'll work on
Practice the exact kind of work Sql for data science professionals do on the job.
Optimizing Customer Segmentation for Enhanced Marketing Strategies - Theme: Global E-commerce Giant
Predicting Customer Churn for Improved Retention Strategies - Theme: Leading Telecommunications Company
Analyzing Sales Data to Identify Profitable Product Categories - Theme: Fast-growing Tech Startup
Optimizing Supply Chain Efficiency through Data-driven Decision Making - Theme: Global Logistics Company
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Sql for data science course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Beginners wanting to learn database querying from scratch.
- Working professionals in marketing or operations looking to analyze their own metrics.
- Career switchers aiming for entry-level data analyst roles in India.
- IT students needing practical SQL and Python projects for their portfolios.
Prerequisites
- No prior coding or database experience is required to begin.
- A computer with internet access to practice writing queries.
- Basic familiarity with spreadsheets is helpful but not mandatory.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete Sql for data science course: free, structured and job-focused
Businesses collect massive amounts of information daily, but raw records are useless without proper analysis. You don't need a math degree to clean, filter, and interpret that data for smart decisions. This is exactly what you'll learn to do across the 34 hours of this program. We start with the Basics of Data Analytics and move quickly into practical skills like Data Collection and Data Wrangling. You will master Exploratory Data Analysis (EDA) to spot hidden trends and apply Statistical Analysis to back up your findings.
As you progress through the 24 modules, you'll pick up multiple tools essential for modern analysts. The curriculum covers Excel for Data Analytics, Python for Data Analytics, and R for Data Analytics. You will also spend significant time on Data Manipulation and Transformation with SQL, learning how to query large databases efficiently. To ensure you can apply these skills, you'll complete four hands-on projects, including Predicting Customer Churn for Improved Retention Strategies and Optimizing Supply Chain Efficiency through Data-driven Decision Making.
LearnTube makes mastering these technical skills highly practical. We curate top YouTube video lessons and pair them with an AI tutor that tests your knowledge. You'll take a quick quiz after every single lesson to reinforce what you just watched. The learning experience is completely free, and you can grab a paid optional certificate when you finish to prove your new database skills to hiring managers.
Jobs you can target after learning Sql for data science
Sql for data science is a gateway skill for some of the fastest-growing roles in tech.
Cleans datasets and builds reports to help business leaders make decisions.
Writes complex queries to extract and manage information within large databases.
Creates visual dashboards and tracks key performance indicators for companies.
Typical entry-level salary ranges in India as of September 2026. Actual compensation varies by city, company and experience.
Earn a Sql for data science certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this Sql for data science course.
Is this SQL course actually free?
Yes. You can watch all 197 video lessons and use the AI tutor quizzes without paying anything. We only charge a fee if you decide to purchase the optional verified certificate at the end.
Do I need to install any software?
No special software is required immediately. You can practice basic queries in online compilers. Later modules covering Python for Data Analytics and R will guide you through setting up free local environments on your computer.
How is this different from watching regular YouTube videos?
We structure the best educational videos into a logical 24-module path. Instead of guessing what to watch next, you get an organized curriculum. Plus, our AI tutor gives you a quiz after every lesson.
Will I get a certificate after finishing?
Yes, a verified certificate is available as an optional paid add-on. You can download it upon completing all modules and quizzes, then easily attach it to your resume or LinkedIn profile to show employers.
Is this program self-paced?
Absolutely. You can learn whenever you have free time. The 34 hours of material are available around the clock, allowing you to study late at night or during weekend breaks at your own speed.
What projects will I build?
You will complete four practical assignments. These include analyzing sales data for a tech startup and predicting customer churn for a telecommunications company, giving you real scenarios to practice your new database querying skills.
Do I need to know Python before starting?
No prior programming knowledge is expected. The curriculum starts with the Basics of Data Analytics before gradually introducing Python, R, and SQL. You will learn the exact syntax needed as you progress through the modules.
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