New Course Enquiry: +1 805 200 4058 (USA) +1 302 360 8582 (USA)
Follow Us:
Python Live online

Python Certification Training for Data Science

CertOcean's Python Certification Training for Data Science training is a live, instructor-led online course. You learn in 20 live sessions (42 hours in total) with an expert instructor, work through 14 curriculum modules with hands-on assignments and keep lifetime access to the class recordings.

4.9Rated Excellent on Trustpilot
  • 42 hoursLive training
  • 20 sessionsWith an instructor
  • 14 modules143 topics
  • Sat, Oct 31Next batch
PY PythonLive instructor-led training
$529 $589 One-time · USD
You save$6010% off
Enroll now
Not sure yet? Talk to a course advisor
  • Pass or full refund, in writing
  • Secure checkout
Course overview

About the Python Certification Training for Data Science training

Python for data science is the most common starting point for a career in data science and machine learning. This course takes you from Python basics to building, tuning and evaluating machine learning models on real datasets.

Why take this Python data science course?

  • You start from zero: the course teaches Python first, then the data science libraries and machine learning.
  • You learn the libraries data scientists use every day: NumPy, pandas, Matplotlib and scikit-learn.
  • Hands-on labs, assignments, quizzes and a final project make sure you can apply what you learn.

This course includes

  • Instructor-led sessions42 hours of live online classes. Weekend batch: 14 sessions of 3 hours each. Weekday batch: 21 sessions of 2 hours each.
  • Real-life case studiesWork on a live project based on a real use case, applying data science with Python.
  • AssignmentsEvery class is followed by a practical assignment, adding up to at least 60 hours of practice.
  • Lifetime accessKeep lifetime access to the LMS, including installation guides, class recordings, quizzes and slides.
  • 24/7 expert supportOur support team is available 24x7 to answer technical questions through a ticket-based system.
  • CertificationComplete the final project to receive CertOcean's Python for Data Science Professional certificate.
  • ForumUse the community forum to discuss questions and learn from your peers.

What you'll learn in this course

  • Write Python programs with sequences, functions, OOP and error handling
  • Analyze data with NumPy and Pandas and visualize it with Matplotlib
  • Clean and manipulate data for analysis
  • Build supervised and unsupervised machine learning models
  • Use dimensionality reduction, association rules and recommendation systems
  • Forecast with time series analysis and improve models with boosting

What will you learn in Python for data science training?

CertOcean's Python Certification Training for Data Science covers the skills a data scientist needs, in this order:

  • Python basics
  • Sequences and file operations
  • Functions, modules, OOP, errors and exceptions
  • NumPy, pandas and Matplotlib
  • Data manipulation
  • Introduction to machine learning with Python
  • Supervised learning I: decision trees, random forests and logistic regression
  • Dimensionality reduction
  • Supervised learning II: Naive Bayes and support vector machines
  • Unsupervised learning and clustering
  • Association rules and recommendation systems
  • Reinforcement learning
  • Time series analysis
  • Model selection and boosting

Why learn Python for data science?

Python is the most popular language for data science because it is easy to read and write, and it has mature libraries for every step of the work. NumPy and pandas handle data, Matplotlib creates charts, and scikit-learn builds machine learning models. A large community also means plenty of tutorials, packages and answers when you get stuck.

Who should take the Python for data science course?

This Python data science course is a good fit for:

  • Programmers and developers
  • Architects and technical leads
  • Analytics managers
  • Information architects
  • Business analysts
  • Python professionals moving into data science
Curriculum

Python Certification Training for Data Science course syllabus

14 modules · 143 topics · 42 hours of live training

Introduction to Python 12 topics
  • Learning goal: Learn the basics of Python and where it is used.
  • Python overview
  • Companies that use Python
  • Applications of Python
  • Running Python scripts on Windows and UNIX
  • Variables, types and values
  • Expressions and operators
  • Loops and conditional statements
  • Command-line arguments
  • Write a "Hello World" program
  • Use loops and conditional statements
  • Work with variables
Sequences and File Operations 12 topics
  • Learning goal: Use Python's sequence types and read and write files.
  • Numbers
  • Strings and string operations
  • Lists and list operations
  • Tuples and tuple operations
  • Dictionaries and dictionary operations
  • Sets and set operations
  • File input and output functions
  • Compare tuples and lists
  • Work with sets
  • Work with dictionaries
  • Work with lists
Functions, OOP, Modules, Errors and Exceptions 15 topics
  • Learning goal: Write reusable Python scripts, organize code into modules and handle errors.
  • Functions and function parameters
  • Global variables, variable scope and return values
  • Lambda functions
  • Object-oriented programming concepts
  • The Python standard library
  • Python modules and import statements
  • The module search path
  • Ways to install packages
  • Error and exception handling, including multiple exceptions
  • Use modules and import options
  • Find and fix errors and exceptions
  • Sort sequences and dictionaries
  • Write lambda functions
  • Use function arguments, keyword arguments and return values
Introduction to NumPy, pandas and Matplotlib 14 topics
  • Learning goal: Learn Python's core data science libraries, basic statistics and data visualization.
  • NumPy arrays and array operations
  • Reading and writing arrays to files
  • pandas data structures and index operations
  • Reading and writing CSV and Excel data with pandas
  • The Matplotlib library
  • Plots, axes and grids
  • Styles, fonts, colors and markers
  • Histograms, pie charts and bar charts
  • Contour plots
  • Create NumPy arrays and run operations on them
  • Create pandas Series and DataFrames
  • Import and export data with pandas
  • Style histograms, bar charts and pie charts
Data Manipulation with pandas 9 topics
  • Learning goal: Clean, combine and explore data for analysis.
  • Core functions of data objects
  • Merging data objects
  • Concatenating data objects
  • Types of joins
  • Exploring and analyzing a dataset
  • Join, merge and concatenate data
  • Aggregate data and use GroupBy
  • Use pandas functions and attributes such as head(), tail(), sum(), std(), iterrows(), itertuples(), items(), values, axes and ndim
Introduction to Machine Learning with Python 9 topics
  • Learning goal: Understand what machine learning is and the main types of machine learning.
  • Python refresher
  • Machine learning basics
  • Machine learning use cases
  • The machine learning process
  • Categories of machine learning
  • Linear regression
  • Gradient descent
  • Build a linear regression model on the California housing dataset
Supervised Learning I: Decision Trees and Random Forests 11 topics
  • Learning goal: Learn classification techniques and implement them in Python.
  • Classification and its use cases
  • Logistic regression
  • Decision trees
  • The decision tree induction algorithm
  • Building an accurate decision tree
  • Confusion matrix
  • Random forest
  • Implement logistic regression
  • Build a decision tree
  • Build a random forest
Dimensionality Reduction 7 topics
  • Learning goal: Understand how the number of features affects a model and reduce dimensions with PCA, factor analysis and LDA.
  • Introduction to dimensionality
  • Dimensionality reduction basics
  • Scaling data before reduction
  • Principal component analysis (PCA)
  • Factor analysis
  • Linear discriminant analysis (LDA)
Supervised Learning II: Naive Bayes and SVM 8 topics
  • Learning goal: Learn more supervised learning techniques and tune them.
  • Naive Bayes and how it works
  • Implementing a Naive Bayes classifier
  • Support vector machines (SVM) and how they work
  • Implementing an SVM
  • Hyperparameter optimization
  • Grid search vs. random search
  • Implement Naive Bayes and an SVM
Unsupervised Learning and Clustering 8 topics
  • Learning goal: Analyze data with clustering and other unsupervised learning methods.
  • Clustering and its use cases
  • K-means clustering and how it works
  • Choosing the optimal number of clusters
  • C-means clustering
  • Hierarchical clustering and how it works
  • Implement K-means clustering
  • Implement hierarchical clustering
Association Rules Mining and Recommendation Systems 8 topics
  • Learning goal: Learn association rules, the Apriori algorithm and how recommendation engines work.
  • Association rules and their parameters
  • Calculating association rule parameters
  • Recommendation engines and how they work
  • Content-based filtering
  • Collaborative filtering
  • Market basket analysis
  • The Apriori algorithm
Reinforcement Learning 12 topics
  • Learning goal: Build agents that learn the best action by interacting with their environment.
  • What reinforcement learning is
  • Elements of reinforcement learning
  • The exploration vs. exploitation dilemma
  • Markov decision process (MDP)
  • Q-learning
  • Policies, Q values and V values
  • The epsilon-greedy algorithm
  • Set up an optimal action
  • Implement Q-learning
  • Calculate optimal quantities
  • Calculate discounted rewards
Time Series Analysis and Forecasting 11 topics
  • Learning goal: Model time-dependent data and forecast future values.
  • What time series analysis is
  • Components and importance of time series analysis
  • White noise
  • Stationarity
  • ACF and PACF
  • AR, MA, ARMA and ARIMA models
  • Check stationarity with the Dickey-Fuller test
  • Convert non-stationary data to stationary data
  • Plot ACF and PACF
  • Build an ARIMA model and plot the forecast
Model Selection and Boosting 7 topics
  • Learning goal: Choose the best model and improve accuracy with boosting.
  • Why model selection matters
  • Cross-validation
  • What boosting is and how it works
  • Types of boosting, including adaptive boosting
  • Cross-validation
  • AdaBoost

Get the detailed syllabus

Receive the full module plan, batch timings and a call with an advisor who can answer your exam questions.

Request syllabus
Upcoming batches

Next Python Certification Training for Data Science batches

Live online classes with an instructor, 9:00 AM to 5:00 PM Eastern Time (ET).

Weekend batch

Starts Saturday, October 31

Starts in 5 days · Live online

Enroll
Weekday batch

Starts Monday, November 2

Starts in 7 days · Live online

Enroll
Flexible

Need another date or time?

Ask for a private or custom schedule.

Ask us
Sample certificate
Certification

Earn your Python Certification Training for Data Science certificate

  • Certificate of completionIssued by CertOcean when you finish the training and assignments.
  • Show it to employersAdd it to your LinkedIn profile and resume.
Start your certification
Learner reviews

Why learners choose CertOcean

Rated Excellent · 4.9 out of 5 on Trustpilot
The training curriculum was great. CertOcean provided me everything required to obtain my CISM. They're excellent!
Laxmi Haya Vadan KaramcheduUnited States · CISMVerified
Very resourceful company, I passed my PMP exam at first attempt, highly recommended.
IjeomaCanada · PMPVerified
Great training support! Helped me pass several certifications.
Cozumel TyUnited StatesVerified
  • US, Canada and UK hoursClasses set around your working day
  • Expert instructorsWorking professionals in your field
  • Career supportResume and interview help
FAQ

Python Certification Training for Data Science course FAQs

Can't find your answer? A course advisor can help with the syllabus, batch dates and exam questions.

Chat on WhatsApp Request a callback
How much does the Python Certification Training for Data Science course cost?

The Python Certification Training for Data Science training costs $529, down from the regular $589 (you save 10%). It is a one-time payment in US dollars, and you can pay securely by card or PayPal.

How long is the Python Certification Training for Data Science course, and when does it start?

The course runs for 42 hours of live, instructor-led online training across 20 sessions. Weekend and weekday batches are available, and the next batch starts on Saturday, October 31, 2026.

What are the prerequisites for the Python data science course?

There are no prerequisites for this Python data science course. You only need a laptop and a good internet connection, and the course teaches Python from the basics.

What will I be able to do after this course?

After the course you can write Python programs, clean and analyze data with pandas, visualize it with Matplotlib, and build and evaluate machine learning models such as decision trees, random forests, SVMs, K-means and ARIMA forecasts. You also receive a CertOcean certificate for completing the course project.

Can beginners learn Python for data science?

Yes. Complete beginners can take this course. It starts with Python basics before moving to data analysis and machine learning, so it works whether you are new to programming or already know some Python.

Will I get a certificate after the Python Certification Training for Data Science training?

Yes. You receive a CertOcean certificate of completion when you finish the training.

Free course consultation

Talk to a Python Certification Training for Data Science advisor

Share your details and we will send the syllabus, the next batch dates and the best plan for your exam date.

  1. 1Tell us your goal and exam date
  2. 2Get the syllabus and batch options
  3. 3Enroll when you are ready
+1 805 200 4058 WhatsApp us support@certocean.com

Get the syllabus and batch dates

Takes under a minute. No payment needed.

Course

Python Certification Training for Data Science

By sending this form you agree to our Terms of Use and Privacy Policy. We never share your details.

More Data Science & ML courses

View all
Save 10%SASSAS AnalyticsSelf-paced training SAS Certification Self-paced · start today $179$199View course Save 17%PYPythonLive training Python Programming Certification Next batch Oct 31 $314$380View course Save 11%MLMachine LearningLive training Machine Learning Certification Training Using Python Next batch Oct 31 $399$449View course Save 10%RR ProgrammingLive training Data Science Certification Training Using R Next batch Oct 31 $430$480View course