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Certification in Data Science using Python

Name: Certification in Data Science using Python
Rating: 4.5 (20 reviews)

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

Created byHuman and Emotion: CHRMI

Last updated 10/2025

English

What you'll learn

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

Course content

10 sections • 48 lectures • 9h 40m total length

Introduction7:58
Module 1. Introduction to Data Science and Python8:43
1.1. Overview of Data Science19:48
Discover how data science blends statistics, machine learning, and domain expertise to extract insights from data, powered by Python for pre-processing, data visualization, predictive modeling, and AI-driven decisions.
1.2. Introduction to Python for Data Science21:07
1.3. Conclusion of Introduction to Data Science and Python6:57

Module 2. Data Manipulation and Cleaning6:58
2.1. Data Import and Export17:04
Master data import and export in Python using pandas to read and write CSV, Excel, JSON, SQL and NoSQL databases, ensuring data integrity, encoding, and reproducibility for data driven workflows.
2.2. Data Cleaning and Preprocessing20:11
2.3. Conclusion of Data Manipulation and Cleaning5:04
Apply data manipulation and cleaning with Python to address missing data, duplicates, outliers, and inconsistencies, preparing high-quality datasets for machine learning and analytics using pandas, NumPy, and scikit-learn.

Module 3. Exploratory Data Analysis (EDA)9:13
Explore exploratory data analysis (EDA) with Python, using pandas, NumPy, matplotlib, and seaborn to visualize data, uncover patterns, detect missing values and outliers, and perform feature engineering.
3.1. Data Visualisation with Matplotlib and Seaborn10:40
3.2. Descriptive Statistics and Data Summarization15:28
Load a csv into a pandas dataframe, inspect shape and data types, and apply descriptive statistics to summarize central tendency, variability, and frequency distribution.
3.2. Descriptive Statistics and Data Summarization 219:25
3.3. Conclusion of Exploratory Data Analysis3:29
Explore exploratory data analysis to reveal distributions, relationships, and anomalies using pandas, NumPy, Matplotlib, and Seaborn. EDA provides descriptive statistics, outlier detection, and feature engineering for machine learning.

Module 4. Statistical Analysis with Python22:16
Explore statistical analysis with Python using NumPy, Pandas, SciPy, statsmodels, and Seaborn to perform descriptive and inferential statistics, visualize with histograms and scatter plots, and enable end-to-end data insights.
4.1. Hypothesis Testing14:21
4.2. Statistical Modelling25:54
4.3. Conclusion of Statistical Analysis with Python5:46
Apply descriptive and inferential statistics with Python to perform hypothesis testing, correlation, and regression. Utilize pandas, NumPy, SciPy, Statsmodels, and seaborn for data preparation, time series forecasting, and visualizations.

Module 5. Machine Learning Basics5:08
Learn machine learning basics, a subset of AI that learns from data to detect patterns and make predictions. Explore data features, model training, testing, and supervised, unsupervised, and reinforcement learning.
5.1. Introduction to Machine Learning10:31
5.1. Introduction to Machine Learning 215:24
5.2. Building and Evaluating Machine Learning Models17:59
5.3. Conclusion of Machine Learning Basics5:57
Master the foundations of machine learning, including supervised, unsupervised, and reinforcement learning, and see how regression, classification, clustering, and dimensionality reduction drive data-driven predictions, evaluation, and deployment.

Module 8. Deep Learning with Python4:10
8.1. Introduction to Deep Learning17:52
Explore deep learning fundamentals, including neural networks, activation and loss functions, optimization with gradient descent and Adam, and practical Python examples using TensorFlow, Keras, and PyTorch.
8.2. Building Deep Learning Models with TensorFlow and Keras20:50
Explore building deep learning models with TensorFlow and Keras, including CNNs for image classification, RNNs and LSTMs for sequential data, GANs for synthetic data, and transfer learning with pre-trained models.
8.2. Building Deep Learning Models with TensorFlow and Keras 210:07
8.3. Conclusion of Deep Learning with Python5:17

Module 9. Big Data Analytics with Python5:00
9.1. Introduction to Big Data Technologies15:25
9.1. Introduction to Big Data Technologies 211:50
Explore basic PySpark transformations and actions on Spark data frames and RDDs, including selection, filtering, and aggregation, and understand the lazy execution model that optimizes large-scale data processing.
9.2. Analyzing Big Data with Spark16:30
Analyze big data with spark using PySpark MLlib, including vector assembler and logistic regression for scalable predictions. Explore distributed data frames and cloud analytics on AWS.
9.3. Conclusion of Big Data Analytics with Python4:49

Module 10. Applied Data Science Projects6:08
10.1. Real World Data Science Projects18:18
10.2. Project Implementation and Presentation19:11
Learn traffic prediction with deep learning and real-time data, healthcare predictive analytics, surveillance object detection, AI resume screening, and stock price forecasting using Python and TensorFlow.
10.3. Conclusion of Applied Data Science Projects6:23
Capstone Project20:00
Develop a capstone project in Python that predicts telecom customer churn using supervised learning, data cleaning, EDA, feature engineering, and models like logistic regression, random forest, and XGBoost.

Requirements

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

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

Who this course is for:

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

Certification in Data Science using Python

What you'll learn

Explore related topics

Course content

Module 1. Introduction to Data Science and Python5 lectures • 1hr 5min

Module 2. Data Manipulation and Cleaning4 lectures • 49min

Module 3. Exploratory Data Analysis (EDA)5 lectures • 58min

Module 4. Statistical Analysis with Python4 lectures • 1hr 8min

Module 5. Machine Learning Basics5 lectures • 55min

Module 6. Machine Learning Algorithms with Python4 lectures • 39min

Module 7. Advanced Topics in Data Science6 lectures • 1hr 4min

Module 8. Deep Learning with Python5 lectures • 58min

Module 9. Big Data Analytics with Python5 lectures • 54min

Module 10. Applied Data Science Projects5 lectures • 1hr 10min

Requirements

Description

Who this course is for: