Overview
You will understand the Introduction to Data Science and Python, starting with the definition, importance, and applications of data science in real-world, You will explore Data Manipulation and Cleaning, covering data import and export from formats like CSV, Excel, and JSON, You will gain expertise in Exploratory Data Analysis (EDA) using visualization tools such as Matplotlib and Seaborn to create bar, line, and scatter plots, You will work with Statistical Analysis in Python, focusing on hypothesis testing with both parametric and non-parametric tests, You will master the Basics of Machine Learning, understanding supervised vs. unsupervised learning, evaluation metrics, You will explore Machine Learning Algorithms in Python, covering supervised learning algorithms like decision trees, random forests, support vector machines, You will advance into Advanced Topics in Data Science covering, Feature Engineering, Deep Learning, and Model Interpretability, You will dive into Deep Learning with Python, building models using TensorFlow and Keras, including convolutional neural networks, You will gain hands-on experience in Big Data Analytics with Python, learning Apache Spark, PySpark, distributed data analysis, You will apply your skills through Applied Data Science Projects, learning how to design, implement, and present data science solutions
Aspiring data scientists, machine learning engineers, and AI enthusiasts who want to build strong foundational skills in Python-based data science., IT professionals, software developers, and analysts looking to transition into data science roles or integrate machine learning into their work., Students, educators, and researchers interested in applying Python and machine learning techniques to real-world data challenges, Business intelligence and analytics professionals who want to expand their skillset into predictive analytics, deep learning, and big data., Anyone curious about data-driven problem solving and eager to gain hands-on experience with tools like Pandas, Scikit-learn, TensorFlow, and Spark
You should have an interest in data science, programming, and how data can be used to extract insights and build intelligent systems., A desire to learn how to collect, clean, manipulate, and analyze data using Python and essential libraries like Pandas, NumPy, Matplotlib, and Seaborn, Interest in real-world data science use cases, including exploratory data analysis, machine learning, deep learning, and big data analytics., Basic familiarity with Python programming (variables, loops, functions) and an understanding of basic math/statistics is recommended.
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
Human and Emotion: CHRMI
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