Course Description:

Welcome to our Certification Program for Data Science using Python!

This course is designed to provide you with the necessary skills and knowledge to become proficient in the field of Data Science using the Python programming language. The program is spread over a period of three months, where you will be introduced to the fundamental concepts of Data Science and Python in the first month, followed by Data Analysis and Visualization in the second month, and finally, Machine Learning and Project-based learning in the third month.

The program is structured to cater to the needs of beginners as well as professionals who are looking to enhance their skill set in the field of Data Science. The course will start by introducing you to the basics of Python programming and its libraries such as NumPy, Pandas, Matplotlib, and Seaborn, which are essential tools for data manipulation, analysis, and visualization. You will then learn how to use these tools to perform exploratory data analysis, data cleaning, and data preprocessing.

The second month of the course focuses on Data Visualization techniques with Matplotlib and Seaborn, Exploratory Data Analysis (EDA) with Pandas, and Data Preprocessing for Machine Learning. You will learn how to create visually appealing and informative plots using Matplotlib and Seaborn, and how to perform EDA to gain insights into your data. You will also learn how to preprocess your data to make it ready for machine learning algorithms.

In the final month of the course, you will delve deeper into Machine Learning algorithms, including supervised and unsupervised learning techniques. You will learn how to implement Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM) for supervised learning, and K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA) for unsupervised learning. The course will culminate in a project where you will apply all the concepts you have learned throughout the program.

By the end of this program, you will be equipped with the skills and knowledge to tackle real-world data science problems using Python programming language. So, get ready to dive into the exciting world of Data Science using Python!

What You will Learn in this Course?

In this course, you will learn:

  • The fundamental concepts of Data Science and its applications
  • How to use the Python programming language and its libraries for Data Science tasks
  • How to manipulate, clean, and preprocess data using Python libraries such as NumPy and Pandas
  • How to visualize data using Matplotlib and Seaborn
  • How to perform exploratory data analysis (EDA) using Pandas
  • How to preprocess data for machine learning algorithms
  • How to implement supervised and unsupervised learning algorithms using Python
  • How to evaluate machine learning models using different metrics
  • How to create a project and apply all the concepts learned throughout the course

By the end of the course, you will be able to perform data analysis and machine learning tasks using Python programming language. You will be equipped with the necessary skills and knowledge to solve real-world data science problems and present your findings in a clear and concise manner.

What are the prerequisite Skills?

What are the prerequisite Skills?

To take this course, you should have some prior programming experience, preferably in Python. You should also have a basic understanding of statistics and linear algebra. Familiarity with concepts such as mean, median, standard deviation, and linear equations would be helpful.

However, even if you do not have prior programming experience or knowledge of statistics and linear algebra, you can still take this course. We will cover the necessary concepts and provide you with the required knowledge to follow along with the course.

Additionally, you should have access to a computer with an internet connection, as well as the ability to install Python and the required libraries. We will provide instructions on how to do this during the course.

Benefits Of the Certification Program

The Certification Program for Data Science using Python offers several benefits, including:

  1. Enhanced Career Opportunities: Data Science is a rapidly growing field with high demand for skilled professionals. Completing this certification program will demonstrate your proficiency in data science using Python, making you more attractive to potential employers.
  2. Skill Development: This program is designed to provide you with the necessary skills and knowledge to become proficient in data science using Python. You will learn a variety of concepts and techniques, including data analysis, machine learning, and data visualization.
  3. Hands-on Learning: The program includes several hands-on projects that will allow you to apply the concepts you have learned in real-world scenarios. This will help you gain practical experience and develop your problem-solving skills.
  4. Recognition and Validation: Upon completion of the program, you will receive a certificate that validates your knowledge and skills in data science using Python. This certificate can serve as proof of your expertise and can be used to support your career growth.
  5. Networking Opportunities: As part of the program, you will be part of a community of learners who share similar interests and goals. This can provide opportunities for networking, knowledge sharing, and collaboration.

Overall, the Certification Program for Data Science using Python is an excellent opportunity to develop your skills and advance your career in data science.

Course Curriculum

Month 1 – Data Science Fundamentals and Python Basics

Week 1: Introduction to Data Science, Python and its Ecosystem

  • What is Data Science?
  • Overview of Python and its libraries: NumPy, Pandas, Matplotlib, Seaborn
  • Installing Python and its dependencies
  • Getting started with Jupyter Notebooks

Week 2: Python Data Structures and Control Flow

  • Variables and Data Types
  • Lists, Tuples, and Dictionaries
  • Control Flow: Conditional Statements and Loops

Week 3: Python Functions and Modules

  • Functions and its types
  • Creating and importing Modules
  • Error Handling

Week 4: NumPy and Pandas for Data Manipulation

  • Introduction to NumPy arrays
  • Indexing and Slicing NumPy arrays
  • Introduction to Pandas
  • Manipulating DataFrames and Series

Month 2 – Data Analysis and Visualization with Python

Week 1: Data Visualization with Matplotlib

  • Introduction to Matplotlib
  • Basic plots: Line, Bar, Scatter, and Histogram
  • Customizing plots with colors, markers, and labels

Week 2: Advanced Visualization with Seaborn

  • Introduction to Seaborn
  • Statistical plotting: Heatmaps, Pair plots, and Violin plots
  • Data visualization best practices

Week 3: Exploratory Data Analysis (EDA) with Pandas

  • Summarizing data with descriptive statistics
  • Data Cleaning: Handling Missing values and outliers
  • EDA techniques: Correlation, Outliers detection, and Hypothesis Testing

Week 4: Data Preprocessing for Machine Learning

  • Feature scaling and Normalization
  • Handling Categorical Variables: One-hot encoding and Label encoding
  • Feature Extraction: PCA and LDA

Month 3 – Machine Learning and Project-based Learning

Week 1: Introduction to Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • Model evaluation metrics: Accuracy, Precision, Recall, F1-score

Week 2: Supervised Learning Algorithms

  • Linear Regression and Logistic Regression
  • Decision Trees and Random Forest
  • Support Vector Machines (SVM)

Week 3: Unsupervised Learning Algorithms

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)

Week 4: Project-Based Learning

  • Applying Machine Learning algorithms on a real-world dataset
  • Implementing EDA, Preprocessing, and Model Selection
  • Presenting findings and results
How will your training work?

Here are the key aspects of how the training will work:

  1. Live sessions: You will attend live online sessions with an experienced instructor, where you will learn the core concepts of Data Science using Python. The instructor will provide live demonstrations, answer your questions, and provide feedback on your work.
  2. Self-paced learning: In addition to live sessions, you will have access to self-paced learning materials, including video tutorials, readings, and practice exercises. These materials will allow you to review concepts at your own pace and reinforce your understanding.
  3. Hands-on projects: Throughout the course, you will work on hands-on projects to apply your knowledge and build practical skills. These projects will allow you to gain real-world experience building data science applications using Python.
  4. Collaboration: You will have the opportunity to collaborate with your peers on projects and assignments, which will allow you to learn from others and share your knowledge and skills.
  5. Feedback and assessment: Throughout the course, you will receive feedback and assessments on your work from the instructor and your peers. This feedback will help you identify areas for improvement and reinforce your understanding of key concepts.
  6. Final project: The final month of the course will be dedicated to a project-based approach, where you will work on a complete application from start to finish. This project will allow you to apply your knowledge and skills and demonstrate your ability to build and deploy data science applications using Python.

By the end of the course, you will have a strong understanding of Data Science using Python and the ability to build and deploy your own applications.

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