Python for Data Science Immersive

at Practical Programming - Online

Course Details
$1,495 30 seats left
Start Date:

Tue, Feb 23, 5:00pm - Mar 25, 8:00pm Central Time (10 sessions)

Next start dates (2)

Purchase Options
Class Level: Beginner
Age Requirements: 18 and older
Average Class Size: 5

Flexible Reschedule Policy: This provider has flexible, free rescheduling for any-in person workshop. Please see the cancellation policy for more details

What you'll learn in this data science course:

Our program serves as the foundation for many well-known concepts of data science. We teach practical techniques and algorithms for extracting and studying useful knowledge from data. This course is not a theory class as we believe there are many ways to learn statistics and analytics concepts on your own. We are providing students with a set of practical tools for data science and knowledge on how to apply Python to solve linear algebra, statistics, and probability problems. This course is designed to fill the gap between theoretical academic research and the needs of the industry. We will start with a crash course on the basics of the Python programming language and then learn how to use Python to turn raw data into insight and knowledge.

What to expect from this course:

Fundamental introduction to Data Science using Python programming language, practical application of different statistical, analytical and linear algebra models to a variety of data science projects, and feeling comfortable enough to apply acquired knowledge on your own seeking a junior data scientist position.

  • Discover best practices for data analysis and start on the path to becoming a data scientist
  • Get comfortable using Python to build statistical and analytical models
  • Learn and practice essential tools for data analytics: NumPy, Pandas and Matplotlib
  • Learn to find solutions to problems by analyzing data using appropriate tools
  • Master your analytical skills by working on real life projects
  • Explore graphical techniques to see what your data looks like
  • Implement the core Data Science techniques of Linear Algebra, Probability, Gradient Descent, and Linear Regression
  • Build your own analytical tools with Python from scratch
  • Become familiar with industry standards and learn the best practices for writing code
  • By the end of this course, you will have a Data Analytics Project to present to potential employers

Data Science Immersive is a registered trade mark of Practical Programming

How this course is organized:

Lecture on new topics takes about 90 minutes and starts at 10.00am. After lecture, students start working on new exercises with instructor guidance. Around 3.00pm students present and discuss their work with instructors, learn alternative solutions, and best practices from instructors and invited data scientist professionals.

Why Python:

Many other programming solutions can be used for Data Science, however we chose high-level programming language Python because it is easy to learn if you are new to programming and it is in high demand.

Python for Data Science Immersive Syllabus

Day 1

  • Variables
  • Numeric Data Types: Int & Float
  • Sequential Types: Str & List
  • Definite Loops: For loops
  • If-Elif-Else Statements
  • Data Types: Tuples
  • Practical Exercises

Day 2

  • Creating Custom Functions
  • Indefinite Loops: while loops
  • Data Types, Dictionary and Set
  • Slicing Data Types
  • Reading txt files with Python
  • Practical Exercises

Day 3

  • NumPy Array
  • Broadcasting and U-Functions
  • Introduction to Pandas
  • Series, DataFrame, Panel
  • Practical Exercises

Day 4

  • 5 ways to create a DataFrame
  • Slicing and Filtering DataFrame
  • Lambda
  • Run If and Else scenarios
  • Gather and Manipulate Data with Pandas
  • Practical Exercises

Day 5

  • Get live data from APIs
  • Plot data with Matplotlib
  • Merge and Concat Data
  • Groupby in Pandas
  • Final project
  • Review and Q&A

Remote Learning

This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.

Upon registration, the instructor will send along additional information about how to log-on and participate in the class.

Still have questions? Ask the community.

Refund Policy
If you withdraw two days before the course start date, any deposit paid will be refunded in full. 

No refunds will be given for cancellations made after that date. If you cannot attend our class or workshop, for which you were charged, you will receive 25% discount for any other course.
Start Dates (3)
Start Date Time Teacher # Sessions Price
5:00pm - 8:00pm Central Time TBD 10 $1,495
This course consists of multiple sessions, view schedule for sessions.
Thu, Feb 25 5:00pm - 8:00pm Central Time TBD
Tue, Mar 02 5:00pm - 8:00pm Central Time TBD
Thu, Mar 04 5:00pm - 8:00pm Central Time TBD
Tue, Mar 09 5:00pm - 8:00pm Central Time TBD
Thu, Mar 11 5:00pm - 8:00pm Central Time TBD
Tue, Mar 16 5:00pm - 8:00pm Central Time TBD
Thu, Mar 18 5:00pm - 8:00pm Central Time TBD
Tue, Mar 23 5:00pm - 8:00pm Central Time TBD
Thu, Mar 25 5:00pm - 8:00pm Central Time TBD
9:00am - 4:00pm Central Time TBD 5 $1,495
This course consists of multiple sessions, view schedule for sessions.
Tue, Mar 30 9:00am - 4:00pm Central Time TBD
Wed, Mar 31 9:00am - 4:00pm Central Time TBD
Thu, Apr 01 9:00am - 4:00pm Central Time TBD
Fri, Apr 02 9:00am - 4:00pm Central Time TBD
9:00am - 4:00pm Central Time TBD 5 $1,495
This course consists of multiple sessions, view schedule for sessions.
Sat, Apr 24 9:00am - 4:00pm Central Time TBD
Sat, May 01 9:00am - 4:00pm Central Time TBD
Sat, May 08 9:00am - 4:00pm Central Time TBD
Sat, May 15 9:00am - 4:00pm Central Time TBD

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Reviews of Classes at Practical Programming (378)

(378 Reviews)
Python for Data Science Immersive
Reviewed by David G. on 11/24/2019
I loved the class a lot because of the fact that it was a week and able to compress all that information into it. I also liked the 10AM start, not too early. The 5pm end was a little late for my particular week that week, since I had things to do for about 3 days of the week and had to leave early. However, any week-long class will present that problem with trying to fit errands before or after the class. The teacher was very fast and he could type as fast as he could talk. I tried to keep up and coded along with him as he presented, which really helped with the hands on and understanding being able to actually see it run in front of me. This type of hands on also keeps me engaged versus just sitting there watching someone lecture. Sometimes I couldn't quite keep up, but all is good, I still got the main points and lots of information in a short time. The exercises were also good practice, but with me, it takes time and experience to be able to rattle off code with intuitive understanding, so I'm not worried about that yet, since with practice, I know it becomes second nature. Overall, I like the way Practical Programming teaches, and I'm thinking of taking more classes from there.
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