What will you learn in Data Science with Python Certification Course
Learn data science fundamentals using Python programming
Explore data wrangling, EDA, and statistical analysis
Work with libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and Seaborn
Build machine learning models including regression, classification, and clustering
Apply concepts through real-world projects using Jupyter Notebooks
Prepare for careers in data science, analytics, and machine learning
Program Overview
Module 1: Python for Data Science
⏳ 1 week
Topics: Python basics, data types, functions, file handling
Hands-on: Create scripts, manipulate data, and solve Python problems
Module 2: Data Analysis & Visualization
⏳ 1 week
Topics: Pandas, NumPy, Matplotlib, Seaborn for data analysis and charts
Hands-on: Analyze and visualize datasets using bar charts, histograms, and heatmaps
Module 3: Statistical Computing & Probability
⏳ 1 week
Topics: Descriptive statistics, probability distributions, hypothesis testing
Hands-on: Perform t-tests, chi-square tests, and build statistical reports
Module 4: Machine Learning Algorithms
⏳ 2 weeks
Topics: Supervised vs unsupervised learning, linear/logistic regression, SVM, clustering
Hands-on: Build, train, and evaluate machine learning models using Scikit-learn
Module 5: Time Series & Text Data Analysis
⏳ 1 week
Topics: Time series forecasting, NLP basics, sentiment analysis
Hands-on: Predict trends using ARIMA and extract features from text
Module 6: Capstone Project & Interview Prep
⏳ 1 week
Topics: Real-life data science problem, resume prep, interview Q&A
Hands-on: Solve a business problem using end-to-end data science techniques
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Job Outlook
Data science professionals are in high demand across tech, finance, and healthcare
Common roles include Data Scientist, Data Analyst, ML Engineer, and AI Specialist
Salaries range from $90,000 to $140,000+ globally depending on role and location
Python remains a top language for data science, enhancing job mobility
Explore More Learning Paths
Take your data science skills to the next level with these curated programs designed to enhance your analytical capabilities and boost your career opportunities.
Related Courses
Foundations of Data Science Course – Build a strong base in data analysis, statistics, and programming essentials to prepare for advanced data science projects.
Applied Data Science Specialization Course – Learn how to apply data science techniques to real-world problems, including machine learning and predictive modeling.
Data Science Foundations Specialization Course – Gain practical experience with Python, data manipulation, and visualization to turn raw data into actionable insights.
Related Reading
What Is Python Used For? – Explore the versatile applications of Python, from data science to web development and automation, and understand why it’s a go-to tool for professionals.
Specification: Data Science with Python Certification Course
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FAQs
- Covers Python-based data science workflow from data wrangling to ML modeling.
- Provides hands-on projects and real datasets to build a portfolio.
- Teaches statistical analysis, EDA, and visualization for business insights.
- Prepares learners for job roles like Data Scientist, Data Analyst, or ML Engineer.
- Offers guidance on resume building and interview readiness.
- Teaches regression, classification, and clustering algorithms.
- Covers training, evaluating, and deploying models using Scikit-learn.
- Includes time series forecasting and basic NLP for text data.
- Provides hands-on exercises to apply ML concepts to real-world problems.
- Prepares learners to implement ML solutions in professional projects.
- Basic Python knowledge is recommended but not mandatory.
- Introduces Python basics, data types, functions, and file handling.
- Gradually progresses to libraries like NumPy, Pandas, Matplotlib, and Seaborn.
- Hands-on exercises help beginners gain confidence in Python for data tasks.
- Suitable for learners from non-technical backgrounds with interest in data science.
- Includes end-to-end projects using real datasets in Jupyter Notebooks.
- Teaches analysis, visualization, and model deployment workflows.
- Covers statistical reporting, business insights, and data storytelling.
- Helps learners showcase project work for portfolios and interviews.
- Encourages practical application of theoretical concepts to business problems.
- Teaches EDA and data visualization with Matplotlib and Seaborn.
- Covers descriptive statistics, probability distributions, and hypothesis testing.
- Guides learners to extract insights and communicate findings effectively.
- Includes visual storytelling techniques for dashboards and reports.
- Prepares learners to make data-driven decisions in professional settings.
