Data Science with Python

However, Python is only a tool used by data scientists for analyzing complex data. To become a data scientist, you need to learn how to analyze data using various tools and libraries in the ecosystem such as NumPy, SciPy, Pandas, and Plotly. Keep in mind that the ability to extract, load, transform, reshape, and visualize data using Python is the key to the success.

This course focuses on teaching the necessary coding, data wrangling, data analysis, and visualization skills required for data scientists. Do you want to know how we help students maximize their learning effectiveness through hands-on projects, coding quizzes, presentations, strong teaching guidance, and support?

Fill out the inquiry form to learn more about the Data Science course curriculum or to talk to our learning advisor.

At a Glance

Intermediate

What you will learn

In this course, we start by introducing Python programming fundamentals including coding basics, data types, functions & modules, and then we will teach Pandas DataFrame for data munging and analysis, NumPy and SciPy for numerical computing, Matplotlib, Seaborn and Plotly for visualization, and Scikit-Learn for predictive modeling.

You will put these tools to use by implementing your own portfolio project and learn how to articulate your analysis through technical and business presentations.

Summary

Become interview ready via a ton of exercises

Learn to manipulate data like a pro

Receive project guidance from experts

Get feedback on your presentations and soft skills

Part-time

Online Live

5 weeks

About the Program

This 5-week Data Science with Python course is one of WeCloudData’s signature course products. Hundreds of students have participated in this course and taken their data analytics skills to the next level. You will meet hiring managers, product managers, directors, and many other professionals who come to this class to polish their Python skills. The course is practical by design, and students learn how to carry out end-to-end data analysis, discover insight, and communicate analytical results through hands-on project implementations.

for those who want to

Learn Python programming and data wrangling skills
Learn how to perform advanced analytics using Python
Build a data science portfolio with WeCloudData's expert guidance and mentorship
Learn end to end data science workflow and how data science can be used to improve your product and business

Speak to our advisor

Our Program Advisor can answer all your questions and help you pick a program that best suits your need. Please fill in your information below and we will contact you.

You can also contact us at info@weclouddata.com or (647) 588-4206

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What you will learn

Hands-on Learning
There's nothing textbook about our approach. With hands-on projects taught by data scientists who actually work in the field, you will learn the intuition, methodology, and end-to-end data science pipeline that you won't be able to learn from MOOCs. In this course, you will learn Python programming, Pandas DataFrame for data munging and analysis, NumPy and SciPy for numerical computing, Matplotlib, Seaborn and Plotly for visualization, and Scikit-Learn for predictive modeling. You will put these tools to use by implementing your own portfolio projects. Instructors will guide you on project scoping, data source research, data collection/scraping, as well as data analysis and presentation. It is a very business-driven and agile environment where students work either in groups or individually to tackle real-life data problems.

Python Programming: programming logic, syntax, data types, flow control, functions and modules
Data wrangling with Pandas DataFrame
Data visualization with Plotly, Seaborn, and Matplotlib
Statistics and linear algebra basics with NumPy and SciPy
Implementation of your first ML algorithm from scratch
Learn end to end data science process
Don't become a "data scientist" who only knows how to work with Jupyter Notebook and Python packages. The job market needs candidates who understand things end to end and know how to collect data by writing a scraping job or calling a Data API, transform data into different shapes, discover insight via visualization, and do predictive analytics to support decision sciences.

Decide how to scope a data project under time/budget constraints
Set up data collection strategy
Perform exploratory data analysis
Transform, merge and aggregate data for predictive modeling
Build regression or classification models
Summarize and visualize analytical insights
Communicate and suggest business strategies via presentations and documentation
Use Pandas DataFrame to work with various data structures
Work with different data sources (csv, text, json, gzip, parquet, database)
Filter and transform data using Pandas and NumPy
Merge multiple datasets using DataFrame
Aggregation data by columns using dataframe.groupby
Use WINDOW functions to deal with rolling windows
Treat missing and extreme values
Visualize data using Python
A plot is worth more than a thousand words! A data scientist will use visualization techniques for two purposes: 1) to explore and understand the data and 2) to communicate data insights to the business team. In this course, you will learn:

Basic plotting using Matplotlib and Pandas
Data exploration using Seaborn
Visualize geolocation data using Folium and GeoPandas
Build and deploy interactive dashboard using Plotly Dash
Learn math for data science
In this module, you will learn enough math so that you can interpret a statistical distribution, handle data with skewed distributions, explore correlations between variables, perform regression analysis and interpret the results, and understand statistical testing and sampling.

Introduction to statistics and SciPy
Introduction to linear algebra and NumPy
Regression analysis 101
Build data science portfolio projects
Practice makes perfect. Students will come up with their own ideas and work on two capstone projects in this course so they can use them for their job search. Original project ideas with strong motivation are always suggested. Throughout the process, students receive guidance from instructors on how to get started and frequently discuss the progress. Instructors who are industry experts will teach students how to turn the project into a proper portfolio that can be demonstrated to a hiring manager.

Problem framing and scoping
Writing the project requirements
Data collection through web scraping or data APIs
Data visualization and analysis
Insight analysis and project documentation
Creating a portfolio through blogging and Github

Resources

Watch our recorded webinar and learn more about Data Science career and industry insights.

Why WeCloudData?

Meet Your Faculty: Tanya Zhou

Instructors & Guest Speakers

Rogelio Cuevas

Director, Data Scientist (Sun Life) | Data Science Instructor (WeCloudData) | Career Mentor (WeCareer)

Rogelio Cuevas currently works as a Director of Data Science in the financial industry in Toronto and as a Data Science mentor for a San Francisco start-up. His educational background has allowed him to work in various research-oriented institutions that include Duke University, McMaster University, University of Western Ontario, and Fermilab. His past research activities made him a recipient of a postdoctoral Fellowship awarded by the Natural Science and Engineering Research Council of Canada.

Ken Ding

Senior Manager, Credit Risk Modeling - Decision Science, BMO Financial Group | Data Science Instructor | Career Mentor

Ken has over 9 years of experience in data analytics and specializes in retail lending, campaign analytics and credit risk modeling. With a passion for Data Science, Ken has acquired solid technical skills (SQL/Python/SAS/AWS) and would like to share his learning experience with everyone. Ken also received an MBA degree from Rotman Business School, University of Toronto.

Tanya Zhou

Data Developer, Walmart

Tanya is currently a Data Developer at Walmart. She holds a Master’s degree in Statistics and Math from McMaster University. In her day to day work, Tanya performs data analytics on large datasets (terabytes) residing on S3 using SQL / Python / Scala with Apache Spark in order to make data-driven decisions. She is a passionate teacher and is currently an assistant instructor of the SQL course at WeCloudData.

Sonia Chhay

Assistant Instructor & Program Manager @WeCloudData

Sonia is a Data Science Assistant Instructor and Program Manager at WeCloudData. She graduated with a double major from UofT in Statistics and Cognitive Science (Computational Cognition). In her roles, some of the responsibilities she takes part in include leading labs and creating SQL, Python, and Machine Learning materials for students. She also oversees the improvement of students’ learning experience and ensures that their learning journey goes smoothly.

Online Learning Platform

Learn anywhere, anytime

Track your learning journey

Watch lecture recordings, work on coding challenges, ask for TA help, and get resume and job support. The learning portal allows you to track your entire learning journey with ease.

Sharpen your coding skills

Leverage our online coding tool to test your knowledge, identify your weaknesses, and improve your Python and SQL coding skills. The LeetCode style live coding challenges will help you get prepared for technical job interviews.

PORTFOLIO PROJECTS

Build data science portfolio projects

Practice makes perfect. Students will come up with their own ideas and work on an end-to-end capstone project in this course so they can use it for their job search. Original project ideas with strong motivation are always recommended.

Build your unique data science project

Learn how to define the use case and build a data analysis strategy
Develop Python scripts to collect data via web scraping or web APIs
Define data models and load data into databases
Use Python and SQL to clean and analyze data
Prepare data for analytics and insights
Data visualization using Python libraries such as Plotly Dash or Streamlit
Present your project along with the insights to experienced data science experts

STUDENT PROJECT DEMO

Reviews

What our students are saying

 5/5

Excellent experience in Weclouddata! The courses are well-organized and keeping up with the ever-evolving data science trend. Each lecture covers a specific DS topic and theory, followed by a real coding demo session, which is really valuable for a data course. Plus, the assignment for each lecture is fully leveraging what has already been learned and requires students’ efforts to dig deeper — I only wish I would have more time after work to go over all of them. A TA session is also designed for each course, which usually occurs once every week or more often. I can always feel free to raise questions there or even offline on Slack. That’s quite supportive!”

– Shelley Geng –

 5/5

I started this course without formal programming or statistic/math background. I really enjoyed the top-down approach used by the Instructor, not only does it keep you interested when learning the libraries and syntax, it also drives you to peruse more self-learning. The instructor along with the TA was very approachable and encouraging. I enjoyed showing them where I was in regards to the class projects and getting feedback on what I could try next. The live coding made the class engaging and not only helped show the process that a developer might take when solving a problem. This class was a great experience and I would definitely recommend it to not only those seeking careers in data science but for those who just want a general taste in programming as well.

– Sinthushan Sooriyakumar –

Schedule, Tuition & Financing Options

Worldwide Online

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WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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I would like to receive the newsletter to keep updated about WeCloudData courses, events and job board information

About the Program

Speak to our advisor

What you will learn

Hands-on Learning

Learn end to end data science process

Use Pandas DataFrame to work with various data structures

Visualize data using Python

Learn math for data science

Build data science portfolio projects

Resources

Instructors & Guest Speakers

Learn anywhere, anytime

Track your learning journey

Sharpen your coding skills

PORTFOLIO PROJECTS

Reviews

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View our Data Science with Python course package

View our Data Science with Python course package

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