Python Data Science Intensive

Learn Python libraries for Data Science: essential tools for working with data

8 sessions
3 hours each
24 Hrs total
Live Online

Full price

$ 899

Bundle and save

4-times payment

$ 224
Klarna

Buy now, pay later

$ 74
Affirm

Upcoming Enrollment

April 23rd - May 16th

8 sessions 3 hours each

Online, US Eastern Time

Share it

Looking for a data science certification to secure a new job or enhance your current career? This course is the perfect place for you.

In our Python Data Science Intensive course, you'll acquire the fundamental skills crucial for a Data Science career, including the ability to gather, describe, and interpret data. Dive into the world of Python packages for Data Science with an emphasis on tools like Pandas, NumPy, and Matplotlib. By unlocking the full potential of Python for data science, you'll pave your way to becoming a proficient data analyst.

Data analytics and data science rely on modern tools such as Python's Pandas and NumPy. These tools allow professionals to handle large datasets and determine relationships and impacts within the data. Our online Python for Data Science certification will train you in the use of these essential tools. In our highly practical course, we place emphasis on industry standards and the requirements for Data Analyst and Data Scientist roles.

This comprehensive Python for Data Science online course will help you master the essential skills of gathering, manipulating, and analyzing data. Using real-life examples, we'll learn how to extract valuable information from sources such as Excel, CSV files, and more sophisticated APIs using Python libraries for Data Science.

Working with large amounts of numbers requires the use of Python's Pandas and NumPy packages . That's why, in this course, we will dedicate substantial time to understanding the DataFrame structure. DataFrames come equipped with a wide array of built-in functions and methods, facilitating seamless data manipulation, filtering, and transformation. They allow users to perform complex data operations using just a few lines of code, significantly reducing the time and effort required in the data preprocessing stage.

In Python for Data Science, DataFrames find applications across the entire data analysis pipeline. From data cleaning and preprocessing to exploratory data analysis, modeling, and visualization, DataFrames streamline the entire process. They facilitate data exploration, enabling users to quickly summarize and analyze data, making it easier to draw meaningful insights.

Interpreting data relies heavily on visualization. Applicants for Data Science and Digital Analytics teams should be able to showcase a diverse range of analysis and visualization techniques. For this reason, in our online Python for Data Science certification, we instruct participants on conducting Exploratory Data Analysis (EDA) using Python libraries for Data Science, specifically Matplotlib and Seaborn.

It is impossible to imagine data science and data analysis without statistics. Our Python for Data Science online course demonstrates how to do statistical analysis in python. In addition to imparting practical knowledge of statistics with Python, such as python for probability statistics, we ensure our participants are well-versed in all core statistical concepts.

To help our students land their dream jobs in data science and data analytics, we work on real-life projects during the course. These projects can be used to build a data science project portfolio.

Additionally, we equip them for common job interview questions. Personal consultations with the instructor are also available for one-on-one guidance.

What to expect

This is an online live instructor-led course, Python Data Science Intensive, which spans eight intensive night sessions or four immersive day sessions, totaling 24 hours of immersive learning. Experience real-time interaction as the instructor addresses your questions and, if necessary, offers code review and error correction. Benefit from comprehensive course notes and training videos to reinforce your understanding of the material. Access the original recordings of the live online sessions for your convenience. Additionally, enhance your skills with carefully curated exercises for extra practice. Even after the course, our dedicated instructor offers continued support via Slack, readily assisting with any questions or clarifications you may need.

Prerequisites

Before enrolling in Python Data Science Intensive, we recommend having completed Python Intensive or possessing a strong knowledge of Python built-in data types and their main characteristics, including mutability. Ensure you have a solid understanding of dictionaries and can work with them proficiently. Familiarize yourself with concepts like function mapping and lambda expressions to make the most of the course.

Syllabus

  1. 1

    Session One

    Understanding the Pandas library and its role in data science. Exploring the built-in data structures: Series and DataFrame. Learning how to create a DataFrame from scratch. Mastering DataFrame slicing and manipulation techniques.

  2. 2

    Session Two

    Understanding the role of NumPy in scientific computing and data analysis. Exploring the advantages of NumPy arrays over Python lists for numerical computations. Learning to create NumPy arrays using various methods. Exploring array attributes such as shape, dimensions, and data type. Understanding the difference between one-dimensional and multi-dimensional arrays.

  3. 3

    Session Three

    Understanding the importance of filtering data to extract relevant information. Learning the basic principles of DataFrame filtering using Boolean masks. Applying conditional expressions to create filters for row selection. Performing advanced filtering operations with multiple conditions using logical operators.

  4. 4

    Session Four

    Understanding the role of logical statements for data manipulation in Pandas. Mastering the use of the apply function to apply custom operations to DataFrame elements. Leveraging the power of np.where for conditional value assignment. Applying logical statements with loc to filter rows and select specific data subsets. Complex Logical Statements and Multiple Conditions

  5. 5

    Session Five

    Introducing Matplotlib as a powerful Python library for data visualization. Setting up the development environment and installing Matplotlib. Learning the fundamental concepts of creating basic plots with Matplotlib. Exploring different plot types, including line plots, scatter plots, and bar plots. Customizing plots with labels, titles, legends, and color schemes.

  6. 6

    Session Six

    Learning the syntax and usage of the query() function. Understanding the benefits of query-based filtering in large datasets. Performing data manipulations and filtering using chained operations. Handling Missing Data and NULL Values. Merging and concatenating DataFrames.

  7. 7

    Session Seven

    Understanding JSON data formats commonly used in APIs. Parsing and converting JSON data into DataFrame structures. Handling nested data structures and data normalization from APIs. Learning to read data from Excel files into Pandas DataFrames. Working with different Excel formats, sheets, and named ranges. Handling data import challenges and data cleaning during import.

  8. 8

    Session Eight

    Understanding the groupby() function for data grouping. Utilizing Pandas functions for time-based data analysis. Resampling time series data for different time frequencies. Learning to apply aggregation functions to compute summary statistics. Exploring multi-level aggregation and handling hierarchical data. Exploring the benefits of MultiIndex for handling complex data structures.

Schedule & Enrollment

Instructors

Art Yudin

Testimonials

I loved Art! He’s super patient and answers all questions! I recommend this class for all python beginners!! I had no knowledge of python prior to this class

Audrey T.

Always enjoy taking a class with Art. He is knowledgeable and on top of his game. His class is quick paced but easy to follow... is patient with questions

Ping Feng

I took Introduction to Python and Web Scrapping with Art and the class was great!

Bryndee Carlson

Took Python for Data Science Immersive with Art. Great instructor! Boost my knowledge within very short time frame!

Rahim Yakubjanov

Art is very thorough, helpful and able to break down content in a way it is easily digestible by those in attendance.

Maximilian K.

Art is great. Very generous with time and knowledge and truly helpful. Great if you have no programming knowledge or if you're a more advanced student.

Adriana Rodriguez

Art is super helpful and attentive to every question.

Hasan Hachem

Art teaches Python in a very understandable way.

Ray Shah

Art has a ton of experience in teaching the basics of python to people who have no previous coding experience. The class format is great - we start with lectures and go right into practice problems to use what we just learned. 100% would recommended!

Helen Li

Art does a great job meeting everyone at their level and making sure that everyone feels challenged.

Tibisay Salrno

Questions? Check here