Certification in Data Science using Python

Learn Data Manipulation and Cleaning, Exploratory Data Analysis, Statistical Analysis, Basics of ML ML in Python

Description

Description

Take the next step in your data science and Python journey! Whether you’re an aspiring data scientist, analyst, machine learning engineer, or business leader, this course will equip you with the skills to harness Python and modern analytics techniques for real-world data-driven solutions. Learn how tools like Pandas, Scikit-learn, TensorFlow, Keras, and Spark are transforming the way organizations analyze data, make predictions, and build AI-powered applications.

Guided by hands-on projects and case studies, you will:

• Master foundational data science concepts and Python workflows applied to real datasets.

• Gain hands-on experience in collecting, cleaning, and manipulating data using libraries like Pandas and NumPy.

• Learn to visualize, analyze, and model data using Matplotlib, Seaborn, and machine learning algorithms.

• Explore advanced topics such as feature engineering, neural networks, deep learning, and big data analytics with PySpark.

• Understand best practices for model evaluation, explainability, and communicating insights effectively.

• Position yourself for a competitive advantage by building in-demand skills at the intersection of programming, data science, and artificial intelligence.

The Frameworks of the Course

· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises—designed to help you deeply understand how to apply Python for data science and machine learning.

· The course includes industry-specific case studies, coding exercises, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to collect, analyze, and model data effectively.

· In the first part of the course, you’ll learn the basics of data science, Python, and essential data handling skills.

· In the middle part of the course, you will gain hands-on experience performing exploratory data analysis, applying statistics, building machine learning algorithms, and working with big data tools like Spark.

· In the final part of the course, you will explore deep learning, model interpretability, advanced analytics, and complete real-world projects. All your queries will be addressed within 48 hours, with full support provided throughout your learning journey.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to Data Science and Python

1.1. Overview of Data Science

1.2. Introduction to Python for Data Science

1.3. Conclusion of Introduction to Data Science and Python

Module 2. Data Manipulation and Cleaning

2.1. Data Import and Export

2.2. Data Cleaning and Preprocessing

2.3. Conclusion of Data Manipulation and Cleaning

Module 3. Exploratory Data Analysis (EDA)

3.1. Data Visualisation with Matplotlib and Seaborn

3.2. Descriptive Statistics and Data Summarization

3.3. Conclusion of Exploratory Data Analysis

Module 4. Statistical Analysis with Python

4.1. Hypothesis Testing

4.2. Statistical Modeling

4.3. Conclusion of Statistical Analysis with Python

Module 5. Machine Learning Basics

5.1. Introduction to Machine Learning

5.2. Building and Evaluating Machine Learning Models

5.3. Conclusion of Machine Learning Basics

Module 6. Machine Learning Algorithms with Python

6.1. Supervised Learning Algorithms

6.2. Unsupervised Learning Algorithms

6.3. Conclusion of Machine Learning Algorithms with Python

Module 7. Advanced Topics in Data Science

7.1. Feature Engineering

7.2. Deep Learning and Neural Networks

7.3. Model Interpretability and Explainability

7.4. Conclusion of Advanced Topics in Data Science

Module 8. Deep Learning with Python

8.1. Introduction to Deep Learning

8.2. Building Deep Learning Models with TensorFlow and Keras

8.3. Conclusion of Deep Learning with Python

Module 9. Big Data Analytics with Python

9.1. Introduction to Big Data Technologies

9.2. Analyzing Big Data with Spark

9.3. Conclusion of Big Data Analytics with Python

Module 10. Applied Data Science Projects

10.1. Real World Data Science Projects

10.2. Project Implementation and Presentation

10.3. Conclusion

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