Data Science
The art of data analysis! Learn all the tools you need for a successful start to your first data science project. In addition to the basics of programming, names such as Pandas or NumPy will no longer be foreign to you. Analysing data sets will be easy for you and you will be able to apply your first machine learning methods.
Prior Requirements -
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Basic knowledge of programming
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Must know basics of Python
How to join the track
You can choose this track as part of the local Digital Shaper Program or the remote Code At Home Bootcamp.
While you need to attend in-person for our Digital Shaper Program, the Code at Home Bootcamp provides a convenient option for those unable to access physical locations or seeking a quicker completion pace.
Time
6 months, 5 hours per week or
3 months, 8 hours per week
Certificate
Receive a graduation certificate
by presenting your project
Participants
Over 900 graduates
About
Basics of Python
Use of Python for Data Science and Machine Learning
Learn to use Numpy for Numerical Data
Learn to use Pandas for Data Analysis
Data visualization Techniques
Use of Scikit-Learn for Machine Learning tasks
Outcome
Receive a graduation certificate by presenting your project
Access to the course material for free
Support from expert mentors
What is Data Science?
Learn data science - theory and hands-on-learning with - R or Python
It doesn't matter if you are a beginner or already have some experience. The program is designed so that you can choose which level of experience you want to start with, whether you want to do hands-on or theory courses and whether you prefer to program in Python or R. You decide. Both - the hands-on as well as theoretical - tracks include basic programming exercises at first to get to know the chosen programming language. The hands-on track is made for people who want to learn how to quickly apply machine learning algorithms to solve problems without a dive into the mathematical background. The theory track includes materials and exercises which covers the theoretical (mathematical) background of several algorithms like linear and logistic regression, neural networks or dimensionality reduction. The tracks also include TechLabs Notebooks where you can deepen the knowlegde and practice the skills you learned during the online courses.
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Setup & IntroductionPart A: Setup - Chapter 01: Course Explanation - Chapter 02: Setup your programming Environment - Chapter 03: Coursera Audit Guide Part B: Introduction - Chapter 01: What is Data Science?
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Introduction to Python ProgrammingPart A - Setup - Chapter 01: Variables, Expressions and Statements - Chapter 02: Conditional Execution - Chapter 03: Functions - Chapter 04: Loops and Iterations - Chapter 05: Strings - Chapter 06: Lists - Chapter 07: Dictionaries - Chapter 08: Tuples - Chapter 09: Sets - Chapter 10: Sequence Functions - Chapter 11: Object Oriented Programming
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Data ManipulationPart A: Regular Expressions - Chapter 01: Regular Expressions in Python Part B: Numpy - Chapter 01: List Vs Array in Python - Chapter 02: How to create Arrays and Functions in Python - Chapter 03: Useful Array Attributes of Numpy - Chapter 04: Numpy Array Index to Access Single Element - Chapter 05: Numpy Slicing and Accessing Subarrays - Chapter 06: Split, Reshape and Concatenate Numpy Arrays - Chapter 07: Arithmatic Operations on Arrays Using Numpy - Chapter 08: Summary Statistics in Numpy - Chapter 09: A Boolean Mask on Numpy Arrays Part C: Pandas - Chapter 01: Getting Started with Data Analysis - Chapter 02: DataFrame and Series Basics - Chapter 03: Indexes - Chapter 04: Filtering - Chapter 05: Updating Rows and Columns - Chapter 06: Add or Remove Rows and Columns From DataFrames - Chapter 07: Sorting Data - Chapter 08: Grouping and Aggregating - Chapter 09: Cleaning Data - Chapter 10: Working with Dates and Time Series in Data - Chapter 11: Reading and Writing Data to Different Sources
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Statistics & Data VisualizationPart A: Introduction to Data - Chapter 01: Introductions and Theory - Chapter 02: Introduction to Jupyter Notebooks - Chapter 03: Data Types in Python - Chapter 04: Introduction to Libraries and Data Management - Chapter 05: Continued to Data Basics Part B: Univariate Data - Chapter 01: Introductions and Theory - Univariate Data - Chapter 02: Important Python Libraries - Chapter 03: Tables, Histograms, Boxplots in Python - Chapter 04: Univariate Data Analysis Using NHANES Data Part C: Multivariate Data - Chapter 01: Introductions and Theory - Multivariate Data - Chapter 02: Multivariate Data Selection - Chapter 03: Multivariate Distributions - Chapter 04: Multivariate Data Analysis Using NHANES Data
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Machine Learning- Chapter 01: Fundamentals of Machine Learning and Introduction to Scikit Learn - Chapter 02: Supervised Machine Learning - Chapter 03: Evaluation
Why Data Science?
Data Scientist - The most in demand job in the 21st century!
“Data is the new oil” - a catchy phrase already said in 2006, which becomes more important over time. What makes companies like Google, Baidu or Amazon so powerful? It’s the amount of data they possess and the employees that are able to analyze it. The data volume that is created, as well as the number of companies that want to use this data, grow exponentially every year. There are beautiful and interesting insights hidden in this sea of ever increasing data, waiting to be discovered - and Data Science is the key to decipher all these hidden insights. Therefore the demand for data scientists is increasing. Data Science is a pretty versatile field. Data Scientists are demanded in almost every imaginable field, such as healthcare, banking, logistics, e-commerce and many more. Hence, as a data scientist you are able to solve a lot of different problems, gain important knowledge and deliver value to other people and your business.
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What will you learn?
Data Science is a multidisciplinary domain. In it’s core it is the task of finding valuable insights from data.
How to apply machine learning models to solve problems like churn prediction or customer segmentation
Exploration of raw data to gain useful insights
General programming knowledge in R or Python
How to present your results in a structured and visually appealing way
