Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools 

Utilize languages commonly used by data scientists like Python, R, and SQL 

Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features  

Create and manage source code for data science using Git repositories and GitHub. 

Describe what a data science methodology is and why data scientists need a methodology.

Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.

Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.

Determine appropriate data sources for your data science analysis methodology.

Learn Python - the most popular programming language and for Data Science and Software Development.

Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

Access and web scrape data using APIs and Python libraries like Beautiful Soup.

Play the role of a Data Scientist / Data Analyst working on a real project.

Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.

Apply Python fundamentals, Python data structures, and working with data in Python.

Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

Analyze data within a database using SQL and Python.

Create a relational database and work with multiple tables using DDL commands.

Construct basic to intermediate level SQL queries using DML commands.

Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story

Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble

Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps

Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

Describe the various types of Machine Learning algorithms and when to use them 

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders 

Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation

Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors

Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model