• Define basic Python programming concepts and data types, including variables, lists, dictionaries, loops, and functions.
• Create functions that accept multiple arguments and return multiple values.
• Understand the purpose of iterators in real-world data science workflows.
• Describe the use and purpose of DataFrames and how they can be used to manipulate data with Pandas.
• Plot visualizations with Matplotlib and Seaborn.
• Get acquainted with descriptive and inferential statistics and how to calculate them.
• Calculate combinations and permutations.
• Familiarize yourself with developer tools for data science, including GitHub basics and working with the command line.
• Calculate linear algebra and regression equations.

What Is Data Science?

• Define the workflow, tools, and approaches data scientists use to analyze data.
• Apply the Data Science Workflow to solve a task.

Your Development Environment

• Navigate through directories using the command line.
• Use Git and GitHub to share repositories.

Python Foundations

• Conduct arithmetic and string operations in Python.
• Assign variables.
• Implement loops and conditional statements.
• Use Python to clean and edit data sets.

Project: Complete coding challenges that often appear in data science job interviews, further developing your Python programming skills.

Exploratory Data Analysis in Panda

• Use DataFrames and Series to read data.
• Rename, remove, combine, select, and JOIN data.
• Identify and handle null and missing values.

Data Visualization in Python

• Define key principles of data visualization.
• Create line plots, bar plots, histograms, and box plots using Seaborn and Matplotlib.

Statistics in Python

• Use NumPy and Pandas libraries to analyze data sets using basic summary statistics.
• Create data visualizations to discern characteristics and trends in a data set.
• Identify a normal distribution within a data set using summary statistics and visualization.

Experiments and Hypothesis Testing

• Determine causality and sampling bias.
• Test a hypothesis using a sample case study.
• Validate your findings using statistical analysis (e.g., p values, confidence intervals).

Project: Apply your growing Python and analytical skills to conduct a basic exploratory data analysis and answer questions about a real-world data set.

Linear Regression

• Define data modeling and linear regression.
• Differentiate between categorical and continuous variables.
• Build a linear regression model for prediction using the scikit-learn library.

Train/Test Split

• Describe errors of bias and variance.
• Define overfitting and underfitting.
• Explore k-folds, LOOCV, and three-split methods.

KNN and Classification

• Build a k-nearest neighbors model using the scikit-learn library.
• Evaluate and tune the model using metrics such as classification accuracy/error.

Logistic Regression

• Build a logistic regression classification model using the scikit learn library.
• Describe the sigmoid function, odds, and odds ratios and how they relate to logistic regression.
• Evaluate a model using metrics such as classification accuracy/error, confusion matrix, ROC/AOC curves, and loss functions.

Project: Build and validate linear regression and KNN models based on a provided data set.

Working With API Data

• Access public APIs and get information back.
• Read and write data in JSON.
• Use the requests library.

Natural Language Processing

• Demonstrate how to tokenize natural language text.
• Categorize and tag unstructured text data.
• Perform text classification model using scikit-learn, CountVectorizer, TfidfVectorizer, and TextBlog.

Time Series Data

• Create rolling means and plot time series data.
• Examine autocorrelation on time series data.

Flex Sessions

Explore an additional data science topic based on class interest. Options include: clustering, decision trees, robust regression, and deploying models with Flask.

• Explore fundamental Python programming concepts, including variables, lists, loops, dictionaries, and data sets.
• Leverage programming tools like GitHub and the command line interface to manage data science projects.
• Practice solving coding challenges similar to the questions used in task-based data science interviews.
• Write and run Python functions using multiple arguments.
• Discover how key math concepts like statistical significance and probability distribution are applied throughout data science.

• Demonstrate familiarity with introductory
programming concepts using Python and NumPy
to navigate data sources and collections.
• Utilize UNIX commands to navigate file systems
and modify files.
• Learn to track changes and iterations using Git
version control from your terminal.
• Define and apply descriptive statistical
fundamentals to sample data sets.
• Practice plotting and visualizing data using Python
libraries like Matplotlib and Seaborn.

Project: Apply NumPy and Python programming skills
to answer questions based on a clean data set.

• Design an experimental study with a well-thought-out problem statement and data framework
• Use Pandas to read, clean, parse, and plot data, extracting and rearranging data through indexing, grouping, and JOINing.
• Review statistical testing concepts (p values, confidence intervals, lambda functions, correlation/causation) with SciPy and StatsModels.
• Learn to scrape website data using popular scraping tools.
• Explore bootstrapping, Resampling and building inferences about your data.

Project: Leverage Pandas to apply advanced NumPy and Python skills cleaning, analyzing, and testing data from multiple messy data sets.

• Use scikit-learn and StatsModels to run linear and logistic regression models and learn to evaluate model fit.
• Begin to look at classification models by implementing the k-nearest neighbors (kNN) algorithm.
• Articulate the bias-variance trade-off as you practice evaluating classical statistical models.
• Use feature selection to deepen your knowledge of study design and model evaluation.
• Learn to apply optimization and regularization for fitting and tuning models.
• Dive into the math and theory behind how gradient descent helps to optimize loss functions for machine learning models.

Project: Explore, clean, and model data based on a provided data set, outlining your strategy and explaining your results.

• Define clustering and its advantages and disadvantages as compared to classification models.
• Build and evaluate ensemble models using decision trees, random forests, bagging, and boosting.
• Get acquainted with natural language processing (NLP) through sentiment analysis of scraped website data.
• Learn how Naive Bayes can simplify the process of analyzing data for supervised learning algorithms.
• Explore the history and use of Hadoop, as well as the advantages and disadvantages of using parallel or distributed systems to store, access, and analyze big data.
• Understand how Hive interacts with Hadoop and discover Spark’s advantages through big data case studies.
• Analyze and model time series data using the ARIMA model.

Project: Students will scrape and model their own data using multiple methods, outlining their approach and evaluating any risks or limitations.

• Compare and contrast different types of neural networks and demonstrate how they are fit with back propagation.
• Build and apply basic recommender systems in order to predict on sample user data.
• Work with career coaches to create and polish your professional portfolio.
• Practice with data science case studies to prepare for job interviews.

Project: Choose a data set to explore and model, providing detailed notebook of your technical approach and a public presentation on your findings.