[align=center]
Certification In Data Science Using Python
Published 10/2025
Created by Human and Emotion: CHRMI
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 48 Lectures ( 9h 40m ) | Size: 2.74 GB[/center]

Learn Data Manipulation and Cleaning, Exploratory Data Analysis, Statistical Analysis, Basics of ML ML in Python
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
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
DescriptionTake 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 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to Data Science and Python1.1. Overview of Data Science1.2. Introduction to Python for Data Science1.3. Conclusion of Introduction to Data Science and PythonModule 2. Data Manipulation and Cleaning2.1. Data Import and Export2.2. Data Cleaning and Preprocessing2.3. Conclusion of Data Manipulation and CleaningModule 3. Exploratory Data Analysis (EDA)3.1. Data Visualisation with Matplotlib and Seaborn3.2. Descriptive Statistics and Data Summarization3.3. Conclusion of Exploratory Data AnalysisModule 4. Statistical Analysis with Python4.1. Hypothesis Testing4.2. Statistical Modeling4.3. Conclusion of Statistical Analysis with PythonModule 5. Machine Learning Basics5.1. Introduction to Machine Learning5.2. Building and Evaluating Machine Learning Models5.3. Conclusion of Machine Learning BasicsModule 6. Machine Learning Algorithms with Python6.1. Supervised Learning Algorithms6.2. Unsupervised Learning Algorithms6.3. Conclusion of Machine Learning Algorithms with PythonModule 7. Advanced Topics in Data Science7.1. Feature Engineering7.2. Deep Learning and Neural Networks7.3. Model Interpretability and Explainability7.4. Conclusion of Advanced Topics in Data ScienceModule 8. Deep Learning with Python8.1. Introduction to Deep Learning8.2. Building Deep Learning Models with TensorFlow and Keras8.3. Conclusion of Deep Learning with PythonModule 9. Big Data Analytics with Python9.1. Introduction to Big Data Technologies9.2. Analyzing Big Data with Spark9.3. Conclusion of Big Data Analytics with PythonModule 10. Applied Data Science Projects10.1. Real World Data Science Projects10.2. Project Implementation and Presentation10.3. 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
Homepage