Professional Certification in Data Science using Python
Master Python, data analysis, ML, and AI in 12 weeks. Gain hands-on skills, real projects, and certification for a high-demand data science career.
Instructor
Cognovera
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The Professional Certification in Data Science using Python is an intensive 12-week program designed to equip learners with the complete skill set required to excel in data-driven roles. Covering Python programming, data manipulation, visualization, statistics, machine learning, and deep learning, this course blends theory with hands-on projects to ensure practical, job-ready expertise. You’ll learn to work with real-world datasets, apply advanced analytics, build predictive models, and present insights effectively—preparing you for careers in data analysis, machine learning engineering, and AI development.
Week 1: Python Foundations for Data Science
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1
Day 1: Introduction to Data Science & Python Setup (Anaconda, Jupyter, IDEs) -
2
Day 2: Python Basics – Variables, Data Types, Operators
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3
Day 3: Conditional Statements, Loops, and Functions
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4
Day 4: Python Data Structures – Lists, Tuples, Sets, Dictionaries
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5
Day 5: Hands-on Practice & Mini Project: Basic Data Calculator
Week 2: Python for Data Manipulation
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6
Day 1: Working with Files – Reading/Writing Text, CSV, JSON
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7
Day 2: Introduction to NumPy – Arrays, Indexing, Slicing
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8
Day 3: NumPy Functions – Aggregations, Broadcasting, Linear Algebra
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9
Day 4: Pandas Basics – Series, DataFrame Creation, Indexing
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10
Day 5: Data Manipulation – Filtering, Sorting, GroupBy, Merge/Join
Week 3: Data Wrangling & Cleaning
Week 4: Data Visualization with Python
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16
Day 1: Introduction to Data Visualization – Importance & Principles
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17
Day 2: Matplotlib – Line, Bar, Pie, Histogram
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18
Day 3: Seaborn – Advanced Plots (Heatmaps, Pairplots, Boxplots)
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19
Day 4: Plotly – Interactive Dashboards Basics
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20
Day 5: Visualization Project – Exploratory Data Analysis (EDA)
Week 5: Statistics for Data Science
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21
Day 1: Descriptive Statistics – Mean, Median, Mode, Variance, Std Dev
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22
Day 2: Probability Basics – Distributions & Random Variables
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23
Day 3: Inferential Statistics – Hypothesis Testing & Confidence Intervals
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24
Day 4: Correlation, Covariance, Chi-Square, t-tests, ANOVA
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25
Day 5: Practical Case Study – A/B Testing Simulation
Week 6: Introduction to Machine Learning
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26
Day 1: ML Workflow – Data Splitting, Model Training, Evaluation
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27
Day 2: Supervised vs Unsupervised Learning Concepts
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28
Day 3: Linear Regression – Theory & Implementation in Scikit-learn
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29
Day 4: Logistic Regression – Theory & Implementation
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30
Day 5: Mini ML Project – Predicting House Prices
Week 7: Supervised Learning Algorithms
Week 8: Unsupervised Learning Algorithms
Week 9: Advanced Machine Learning Concepts
Week 10: Introduction to Deep Learning
Week 11: Time Series & Natural Language Processing (NLP)
Week 12: Capstone Project & Career Preparation
1. Do I need prior coding experience?
No, the course starts with Python fundamentals and gradually advances to complex topics.
2. Will there be real-world projects?
Yes, every week includes a mini project, and Week 12 is a capstone project using industry datasets.
3. What career roles can I apply for after this course?
You can pursue roles such as Data Analyst, Data Scientist, Machine Learning Engineer, and Business Intelligence Analyst.
4. How is the course delivered?
The program is available online, in-person, or hybrid with interactive live sessions, recorded lectures, and hands-on labs.
5. Will I receive a certification?
Yes, you will receive a Professional Certification in Data Science using Python upon successful completion.
Skills You Will Learn
- Python programming for data science
- Data cleaning, preprocessing, and wrangling
- Exploratory Data Analysis (EDA) & data visualization
- Statistical analysis & hypothesis testing
- Machine learning model building & evaluation
- Deep learning basics with TensorFlow & Keras
- Time series forecasting & natural language processing
- Feature engineering & model optimization
- Real-world data project execution
- Data storytelling & presentation skills
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Course details
Lectures
60
Level
Intermediate
Basic info
- Course Name: Professional Certification in Data Science using Python
- Duration: 12 Weeks (5 Days/Week)
- Mode: Online / Classroom / Hybrid
- Level: Beginner to Advanced
- Assessment: Assignments, Quizzes, Capstone Project
- Certification: Professional Certification in Data Science using Python
- Projects: Mini projects every week + 1 Capstone Project
- Tools & Libraries Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, Plotly
Course requirements
- Basic computer literacy and internet access
- Familiarity with mathematics at high school level (algebra, basic probability)
- Logical thinking and problem-solving skills
- No prior programming experience required (Python basics will be covered)
Intended audience
- Students & graduates aiming for a career in data science or AI
- Working professionals seeking to upskill for data-driven roles
- IT professionals transitioning into machine learning & analytics
- Business analysts, researchers, and statisticians expanding into Python-based analytics
- Anyone passionate about turning data into actionable insights
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