## Master Python for Real-World Data Science
Launch your data science career by mastering Python, the most popular programming language for data professionals. In this Track, you'll learn how to use Python to import, clean, manipulate, and visualize data—all essential skills for any aspiring data scientist or researcher.
## Gain Hands-On Experience with Python's Top Libraries
Through interactive exercises and real-world projects, you'll get practical experience with the most powerful Python libraries for data science, including:
* pandas for data manipulation
* Matplotlib and Seaborn for data visualization
* scikit-learn for machine learning
* statsmodels for statistical analysis
By working with real datasets, you'll develop the skills needed to tackle complex data science challenges and deliver actionable insights.
## Progress from Python Basics to Advanced Data Science Techniques
Starting with the Python essentials, you'll quickly progress to more advanced topics like exploratory data analysis, statistical hypothesis testing, and predictive modeling with machine learning. The carefully designed curriculum ensures a smooth learning journey, even if you have no prior coding experience.
## Prepare for the Associate Data Scientist Certification
This Track provides comprehensive preparation for the Associate Data Scientist in Python certification. By completing the courses and projects, you'll master the key concepts and techniques covered in the certification exam. Earning this industry-recognized credential will validate your skills and help you stand out in the job market.
## Launch Your Data Science Career with Confidence
Whether you're aiming to become a data scientist, enhance your data analysis skills, or drive data-informed decision-making in your organization, this Track will equip you with the tools and knowledge you need to succeed. By the end, you'll have a strong portfolio of projects demonstrating your ability to solve real-world problems with Python.
Join thousands of learners who have kickstarted their data science careers with this Track. Enroll now and start your journey to becoming a confident, certified data scientist in Python!
Overview
Syllabus
- Introduction to Python
- Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
- Intermediate Python
- Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
- Investigating Netflix Movies
- Data Manipulation with pandas
- Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
- Exploring NYC Public School Test Result Scores
- Joining Data with pandas
- Learn to combine data from multiple tables by joining data together using pandas.
- Introduction to Statistics in Python
- Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
- Introduction to Data Visualization with Matplotlib
- Learn how to create, customize, and share data visualizations using Matplotlib.
- Introduction to Data Visualization with Seaborn
- Learn how to create informative and attractive visualizations in Python using the Seaborn library.
- Visualizing the History of Nobel Prize Winners
- Introduction to Functions in Python
- Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
- Python Toolbox
- Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
- Exploratory Data Analysis in Python
- Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.
- Analyzing Crime in Los Angeles
- Working with Categorical Data in Python
- Learn how to manipulate and visualize categorical data using pandas and seaborn.
- Customer Analytics: Preparing Data for Modeling
- Data Communication Concepts
- No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.
- Introduction to Importing Data in Python
- Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
- Cleaning Data in Python
- Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
- Exploring Airbnb Market Trends
- Working with Dates and Times in Python
- Learn how to work with dates and times in Python.
- Writing Functions in Python
- Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
- Introduction to Regression with statsmodels in Python
- Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
- Modeling Car Insurance Claim Outcomes
- Sampling in Python
- Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
- Hypothesis Testing in Python
- Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
- Experimental Design in Python
- Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
- Hypothesis Testing with Men's and Women's Soccer Matches
- Supervised Learning with scikit-learn
- Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
- Predictive Modeling for Agriculture
- Unsupervised Learning in Python
- Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
- Clustering Antarctic Penguin Species
- Machine Learning with Tree-Based Models in Python
- In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
- Predicting Movie Rental Durations
Taught by
Hugo Bowne-Anderson, Benjamin Wilson, Elie Kawerk, DataCamp Content Creator, Ariel Rokem, Shayne Miel, Richie Cotton, Maggie Matsui, Aaren Stubberfield, Adel Nehme, Kasey Jones, Maarten Van den Broeck, Hadrien Lacroix, James Chapman, George Boorman, and Izzy Weber
