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.

Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.

Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.

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

Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

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.

Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning

Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights

Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines

Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven 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

Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.

Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.

Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.

Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.

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