Python Data Analysis Classes Available |
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Data Analysis Bootcamp 1 - Data Analysis Intro
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Data Analysis Bootcamp 2 - Understanding Distributions |
Data Analysis Bootcamp 3 - Pandas Plotting Hell Week 1 |
Data Analysis Bootcamp 4 Seaborn Univariate Hell Week 2 |
Data Analysis Bootcamp 5 - Bivariate Analysis |
Data Analysis Bootcamp 6 - Hell Week 3 - Seaborn Bivariate |
Data Analysis Bootcamp 7 - Wrangling, Cleaning, Treating |
Data Analysis Bootcamp 8 - Interactive Plotly |
Data Analysis Bootcamp 9 - Hacker Statistics |
Testimonials
"I thought those images were silly, complicated, and pointless, but can now see their value. But I still can remember...."
Bob Sorg
"Through an odyssey of hands-on lessons, I became adept at wrangling unruly datasets into submission"
Vinod Sangare
"...Bootcamp helped me a lot to have a better understanding of what i was doing and improve. I can hardly wait the new things i will find out about ML in Machine Learning Bootcamp!"
Vlad Munteanu
Are you savy with Excel?
Are you interested in a career in Data Analysis?
Are you interested in Remote work?
DataSimple.education's Ai-Enhanced Bootcamp will accelerate your learning experience and propel you into the world of Data Science. This data analysis bootcamp works as a robust foundation to the DataSimple Ai-Enhanced Machine Learning Bootcamp or the DataSimple Ai-Enhanced Deep Learning bootcamp. After completing this program, you will have great confidence in your ability to use essential Python libraries: Pandas, Matplotlib, Seaborn, and Plotly Express.
However, Data Analysis is more than understanding how to use Python and data analytic tools. We need to understand when and why to use these tools and visualizations. We need to go beyond tool mastery and learn to interpret insights and validate their robustness before sharing them with business partners. Data analysis is not just a skill; it's an art and a science intertwined. We aim to help you grasp the essence of data analysis, not just 'how' but 'when' and 'why.'
About DataSimple's Python Data Analysis Bootcamp
Python as a Data Analysis Tool: Python has gained immense popularity in the field of data analysis due to its simplicity and versatility. Its rich ecosystem of libraries, including Pandas for data manipulation, Seaborn for data visualization, and Plotly for interactive visualizations, makes it a preferred choice for data professionals.
Excel vs. Python: While Excel is widely used for data analysis, it has limitations when dealing with large datasets and complex tasks. Python, on the other hand, can handle a wide range of data analysis tasks more efficiently. Learning Python for data analysis is a natural progression for anyone familiar with Excel.
Pandas for Data Manipulation: Pandas is a fundamental library for data manipulation and analysis in Python. It offers powerful tools for data cleaning, transformation, and aggregation, making it essential for anyone working with data.
Seaborn for Data Visualization: Seaborn is a high-level data visualization library built on top of Matplotlib. It simplifies the creation of attractive and informative statistical graphics, making data exploration and presentation more accessible.
Plotly Express for Interactive Visualizations: Plotly is another Python library that excels in creating interactive and dynamic visualizations. It's especially useful for creating dashboards, however Plotly Express offers many unique plots that allow for special high-level analysis not found elsewhere, with the added benefit of interactivity Plotly Express can speed up the understanding of the patterns being seen and extract valuable insights faster than other plotting tools.
Data Story Telling: Turning your insights into a presentation is crucial as it allows you to effectively communicate complex data-driven findings to stakeholders, enabling data to drive informed decision-making. Presentation skills bridge the gap between data analysis and actionable outcomes, making your insights accessible and actionable in the business context.
AI-Enhanced Learning: Traditional education takes the stance that it is the student's responsibility, and not the education provider's, responsibility to ensure the knowledge covered in class is retained. At DataSimple we believe that our responsibility is to not only offer a robust set of information but also to help a student retain the knowledge. We have used Ai to enhance our educational material to ensure the students can easily retain the knowledge learned in class.