Data Science: Python for Data Analysis 2022 Full Bootcamp

Use Coupon ACEDS100 to get the course for FREE(Valid till 11/12/2021)

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.

     Course Contents

  Part 1: Data Science: An Introduction: Foundations of Data Science

  – Welcome (1.1)

  – Demand for Data Science (2.1)

  – The Data Science Venn Diagram (2.2)

  – The Data Science Pathway (2.3)

  – Roles in Data Science (2.4)

  – Teams in Data Science (2.5)

  – Big Data (3.1)

  – Coding (3.2)

  – Statistics (3.3)

  – Business Intelligence (3.4)

  – Do No Harm (4.1)

  – Methods Overview (5.1)

  – Sourcing Overview (5.2)

  – Coding Overview (5.3)

  – Math Overview (5.4)

  – Statistics Overview (5.5)

  – Machine Learning Overview (5.6)

  – Interpretability (6.1)

  – Actionable Insights (6.2)

  – Presentation Graphics (6.3)

  – Reproducible Research (6.4)

  – Next Steps (7.1)

Part 2: Data Sourcing: Foundations of Data Science

  – Welcome (1.1)

  – Metrics (2.1)

  – Accuracy (2.2)

  – Social Context of Measurement (2.3)

  – Existing Data (3.1)

  – APIs (3.2)

  – Scraping (3.3)

  – New Data (4.1)

  – Interviews (4.2)

  – Surveys (4.3)

  – Card Sorting (4.4)

  – Lab Experiments (4.5)

  – A/B Testing (4.6)

  – Next Steps (5.1)

Part 3: Coding

  – Welcome (1.1)

  – Spreadsheets (2.1)

  – Tableau Public (2.2)

  – SPSS (2.3)

  – JASP (2.4)

  – Other Software (2.5)

  – HTML (3.1)

  – XML (3.2)

  – JSON (3.3)

  – R (4.1)

  – Python (4.2)

  – SQL (4.3)

  – C, C++, & Java (4.4)

  – Bash (4.5)

  – Regex (5.1)

  – Next Steps (6.1)

Part 4: Mathematics

  – Welcome (1.1)

  – Elementary Algebra (2.1)

  – Linear Algebra (2.2)

  – Systems of Linear Equations (2.3)

  – Calculus (2.4)

  – Calculus & Optimization (2.5)

  – Big O (3.1)

  – Probability (3.2)

Part 5: Statistics

  – Welcome (1.1)

  – Exploration Overview (2.1)

  – Exploratory Graphics (2.2)

  – Exploratory Statistics (2.3)

  – Descriptive Statistics (2.4)

  – Inferential Statistics (3.1)

  – Hypothesis Testing (3.2)

  – Estimation (3.3)

  – Estimators (4.1)

  – Measures of Fit (4.2)

  – Feature Selection (4.3)

  – Problems in Modeling (4.4)

  – Model Validation (4.5)

  – DIY (4.6)

  – Next Step (5.1)

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2021.

You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.