Learn Data Analytics for Product Managers Online: Free Course with AI Tutor & Certificate
Product managers often struggle to back their roadmaps with hard data. This free product analytics course solves that by teaching you SQL, Python, and R over 197 video lessons. You'll spend about 34 hours mastering exploratory data analysis and predictive analytics, giving you the skills to make confident product decisions.
What you'll learn in this Data Analytics for Product Managers 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.
Gain knowledge of different types of data analytics and the data analytics lifecycle.
Learn data collection methods, including survey data collection, observation data collection, and web scraping.
Develop skills in exploratory data analysis, including descriptive statistics, trend identification, and outlier analysis.
Acquire the ability to perform statistical analysis, including hypothesis testing, regression analysis, and time series analysis.
Data Analytics for Product Managers 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 Data Analytics for Product Managers professionals do on the job.
Data-driven Decision Making: Optimizing Product Strategy for a Global Tech Giant
Customer Segmentation and Personalization: Enhancing User Experience for a Leading E-commerce Startup
Predictive Analytics for Supply Chain Optimization: Streamlining Operations for a Fortune 500 Manufacturing Company
Market Trend Analysis and Forecasting: Driving Business Growth for a Fast-growing Fintech Startup
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Data Analytics for Product Managers course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Product managers wanting to transition into data-heavy roles or back their decisions with metrics.
- Business analysts and aspiring data product managers looking to build a technical portfolio.
- Founders and startup leaders in India who need to analyze user behavior without hiring a full data team.
- Tech professionals aiming to earn a product manager data analytics certification to stand out to employers.
Prerequisites
- No advanced programming experience is required to start this course.
- A basic understanding of product management concepts and business metrics is helpful.
- A computer with internet access to run Python, R, and SQL environments.
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 Data Analytics for Product Managers course: free, structured and job-focused
Guessing what users want doesn't work for modern product development. You need to understand exactly how people interact with your features. This data driven product management course teaches you how to gather, clean, and analyze user data to inform your product strategy. Starting with Understanding Data Analytics, you'll move through 24 detailed modules covering everything from Exploratory Data Analysis (EDA) to Machine Learning Basics. We teach you exactly what you need, including Excel, Python, and SQL for Data Analytics.
Theory alone won't make you a data product manager. That is why you'll apply your new skills across four hands-on projects. You will optimize a product strategy for a global tech giant and handle customer segmentation for a leading e-commerce startup. Later, you'll tackle market trend analysis for a fintech startup and predictive analytics for supply chain optimization. By completing these, you'll know how to perform hypothesis testing, regression analysis, and web scraping.
LearnTube makes mastering product analytics simple and completely free. We take the best instructional videos from YouTube and organize them into a structured learning path. After every single one of the 197 video lessons, an AI tutor gives you a quick quiz to ensure you actually retain the material. You can learn at your own pace, and if you want to prove your new skills to employers, you can upgrade to get an optional paid certificate.
Jobs you can target after learning Data Analytics for Product Managers
Data Analytics for Product Managers is a gateway skill for some of the fastest-growing roles in tech.
Leads product strategy by analyzing user data and defining key performance metrics.
Examines customer behavior data to help product teams optimize features and user experience.
Uses predictive analytics and A/B testing to drive user acquisition and retention.
Typical entry-level salary ranges in India as of September 2026. Actual compensation varies by city, company and experience.
Earn a Data Analytics for Product Managers 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 Data Analytics for Product Managers course.
Is this product analytics course completely free?
Yes, the entire learning experience is free. You can watch all 197 video lessons, take the AI quizzes, and complete the four hands-on projects without paying anything. The only paid feature is the optional certificate at the end.
How does this differ from watching plain YouTube tutorials?
We organize scattered YouTube videos into a logical, 24-module curriculum. Instead of just passively watching, our AI tutor tests your knowledge with a quiz after every single lesson so you actually retain the data analytics concepts you learn.
Will I get a product analytics certification?
You can choose to purchase an official certificate once you finish all the modules and projects. This optional paid add-on is a great way to show hiring managers that you have formal product manager data analysis training.
What software do I need installed for this course?
You will need basic tools like Microsoft Excel, plus environments to run Python, R, and SQL. The course modules will guide you through setting up these data analytics tools on your computer before you start writing any code.
Is this course self-paced or scheduled?
The course is entirely self-paced. You can log in and learn data driven product management whenever you have free time. The 34 hours of video content are always available, making it easy to fit around a busy work schedule.
Do I need prior coding experience to take this?
No prior programming knowledge is strictly required. The curriculum starts with the Basics of Data Analytics before slowly introducing you to Python, R, and SQL, ensuring you build technical skills at a comfortable pace.
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