DS-JUPYTER.AW1
Data Science with Jupyter
Data science sounds hard? Not with Jupyter! This course breaks it down so you can skill up and stand out.
- 21 Interactive Lessons and 128 topics mapped to the official exam objectives
Beginner Self-paced · 1 year access
21Interactive Lessons
128Topics
5Flashcards
5Glossary of terms
01 / Skills you'll get
What you will be able to do
Our Data Science with Jupyter online course offers a beginner-friendly journey into the world of data, starting with Python basics and moving all the way to advanced machine learning (ML) techniques.
You’ll learn how to import, clean, and analyze datasets, create stunning visualizations, and apply feature engineering to make your data shine. From understanding statistics to mastering ML algorithms, this course covers everything!
By the end of this course, you’ll be able to handle data science tasks with ease.
- Master Python basics and advanced concepts tailored for data analysis and machine learning.
- Create insightful and visually appealing charts to communicate data-driven stories.
- Learn to import, clean, and preprocess datasets to make them analysis-ready.
- Understand and apply trending ML algorithms to solve real-world problems.
- Transform raw data into valuable features to improve the performance of ML models.
Course Highlights
-
21 Structured Lessons Comprehensive coverage of core course objectives
-
1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
21 Interactive Lessons · 128 topics01 Preface +
02 Data Science Fundamentals 6 topics +
- What is Data?
- What is Data Science?
- What a Data Scientist actually do?
- Real world use cases of Data Science?
- Why Python for Data Science?
- Conclusion
03 Installing Software and Setting Up 8 topics +
- System Requirements
- Downloading the Anaconda
- Installing the Anaconda in Windows
- Installing the Anaconda in Linux
- How to install a new Python library in Anaconda
- Open your notebook- Jupyter
- Know your notebook
- Conclusion
04 Lists and Dictionaries 8 topics +
- What is list?
- How to create a list?
- Different list Manipulation operations
- Difference between lists and tuples
- What is dictionary?
- How to create a dictionary?
- Some operations with dictionary
- Conclusion
05 Function and Packages 10 topics +
- Help() function in Python
- How to import a Python package?
- How to create and call a function?
- Passing parameter in a function
- Default parameter in a function
- How to use unknown parameters in a function?
- Global and Local variable in a function
- What Is Lambda Function?
- Understanding Main in Python
- Conclusion
06 NumPy Foundation 8 topics +
- Importing a NumPy package
- Why NumPy array over List?
- NumPy array Attributes
- Creating NumPy arrays
- Accessing element of a NumPy array
- Slicing in NumPy array
- Array Concatenation
- Conclusion
07 Pandas and DataFrame 6 topics +
- Importing Pandas
- Pandas Data Structures
- .loc[ ] and .iloc[ ]
- Some Useful DataFrame Functions
- Handling missing values in DataFrame
- Conclusion
08 Interacting with Databases 10 topics +
- What is SQLALchemy?
- Installing SQLALchemy Package
- How to use SQLAlchemy?
- SQLAlchemy Engine Configuration
- Creating A Table In Database
- Inserting Data In a Table
- Update a record
- How to join two tables
- How to join two tables
- Conclusion
09 Thinking Statistically in Data Science 10 topics +
- Statistics in Data Science
- Types of Statistical data/variables?
- Mean, Median and Mode
- Basics of Probability
- Statistical Distributions
- Pearson Correlation Coefficient
- Probability Density Function (PDF)
- Real World Example
- Statistical Inference and Hypothesis Testing
- Conclusion
10 How to import data in Python? 7 topics +
- Importing txt data
- Importing csv data
- Importing Excel data
- Importing JSON data
- Importing pickled data
- Importing a compressed data
- Conclusion
11 Cleaning of Imported Data 8 topics +
- Know your data
- Analysing Missing Values
- Dropping Missing Values
- Automatically Fill Missing Values
- How to scale and normalize data?
- How to Parse Dates?
- How to apply character encoding?
- Conclusion
12 Data Visualization 7 topics +
- Bar Chart
- Line Chart
- Histograms
- Scatter Plot
- Stacked Plot
- Box Plot
- Conclusion
13 Data Pre-processing 6 topics +
- About the case-study
- Importing the dataset
- Exploratory Data Analysis
- Data Cleaning & Pre-processing
- Feature Engineering
- Conclusion
14 Supervised Machine Learning 10 topics +
- Some common ML Terms
- Introduction to Machine Learning (ML)
- List of common ML Algorithms
- Supervised ML Fundamentals
- Solving a Classification ML Problem
- Solving a Regression ML Problem
- How to Tune your ML Model?
- How to handle categorical variable in sklearn?
- Advanced technique to handle missing data
- Conclusion
15 Unsupervised Machine Learning 7 topics +
- Why Unsupervised Learning?
- Unsupervised Learning Techniques
- Clustering
- Principal Component Analysis (PCA)
- Case Study
- Validation of Unsupervised Ml
- Conclusion
16 Handling Time-Series Data 7 topics +
- Why Time-Series is important?
- How to handle Date and Time?
- Transforming a Time Series Data
- Manipulating a Time Series Data
- Comparing Time Series Growth Rates
- How to change Time Series Frequency?
- Conclusion
17 Time-Series Methods 5 topics +
- What is Time-Series forecasting?
- Basic Steps in Forecasting
- Time Series Forecasting Techniques
- Forecast future traffic to a Web page
- Conclusion
18 Case Study-1 2 topics +
- Case Study 1: Predict whether or not an applicant will be able to repay a loan
- Conclusion
19 Case Study-2 1 topics +
- Conclusion
20 Case Study-3 1 topics +
- Case Study 3: Build a film recommendation engine
21 Case Study-4 1 topics +
- Case Study 4: Predict the sales of house
03 / FAQs
Questions before you start
Who is this course for?+
This Jupyter for Data Science course is perfect for beginners who want to start a career in data science, as well as professionals looking to upskill or switch to data science. No prior experience is required.
Do I need to know Python before taking this course?+
No, this Data Science course starts with Python basics and gradually builds up to advanced topics, making it beginner-friendly.
What tools will I learn to use in this course?+
You’ll primarily practice Python and Jupyter Notebook, along with popular libraries like Pandas, NumPy, Matplotlib, and Scikit-learn.
How long does it take to complete this course? +
The Practical Data Science with Jupyter course is self-paced, but most learners complete it in 8-16 weeks with a commitment of 5-7 hours per week.
How does this course compare to free resources like YouTube or Kaggle?+
While free resources are great, this course offers interactive quizzing items, hands-on labs, videos, and practice tests to give you an edge in your career.
I’m bad at Math. Can I still do this course?+
Definitely! The course focuses on practical applications and uses Python libraries to handle complex calculations, so you don’t need to be a mathematician.
Keep going
You might also like
From Beginner to Data Expert in One Course
Learn data science with Jupyter the practical way. Hands-on labs, gamified practice tests, real-world challenges; everything you need is right here!
- 1 year of full access
- Certificate of completion