This class is targeted at aspiring programmers who are learning their first programming language. It may be appropriate for programmers who currently at beginner-intermediate level in another programming language, and want to get a jump start in learning Python. Such students will also clarify their understanding of what aspects of programming languages are broadly shared among languages, and which are idiosyncratic to whatever language they already speak.The majority of this course is dedicated to learning foundational aspects of programming. However, it is often easier to elucidate these through concrete examples. Many of the examples are geared towards applications in analytics, statistics, and artificial intelligence. This focus skews the class slightly, but students will still build a strong enough foundation to learn domain specific skills. It also means, a little bit of prior statistics knowledge will be helpful during the last two classes.

Students who take this class will be able to:

• Identify and describe the foundations of all programming languages, especially the use of:
  • Variables,
  • Common data processing techniques,
  • Control flow,
  • Functions,
  • Scope,
  • Imports, libraries, and APIs
• Use Python to import and process data.
• Work with libraries and APIs in Python.
• Use Python libraries to create charts and graphs.
• Describe artificial intelligence and machine learning at a high level.
• Use Scikit Learn to perform ML tasks.

Your learning experience will be supported by office hours and a Slack channel, where you can ask your questions and get help when you got stuck.

Curriculum

• The Big Picture and The Very Basics
  1. What does it mean to program a computer?
  2. What can programming languages do?
  3. Why Python? How is it different from other languages?
  4. Variables and data types
  5. Combining data with operations
  6. Exercise:
     1. Write and run your first simple script.
     2. Use a debugger to examine code examples.
• Complex Data and Control Flow
  1. Lists and dictionaries
  2. Control Flow (if/elif/else)
  3. Looping
  4. Exercise:
     1. Create and modify lists and dictionaries
     2. Search for items in a list
     3. Create, modify, and select from nested lists and dictionaries
• Functions and Classes
  1. Functions
  2. Classes
  3. Code reuse
  4. Exercise:
     1. Create and use functions to process data
     2. Create and use classes to manage complex data and processes
• Working With Libraries and APIs Part 1
  1. Importing and using libraries and modules.
  2. Reading documentation.
  3. Searching for tutorials and answers to questions.
  4. Using Jupyter Notebooks vs a text editor.
  5. Exercise:
     1. Use Jupyter Notebook, Pandas, and Matplotlib to perform descriptive statistics and create charts.
• AI/ML In Python
  1. What are artificial intelligence and machine learning?
  2. What is supervised learning?
  3. Scikit-learn’s API.
  4. Test/train/validation data splits.
  5. The K-nearest Neighbor algorithm.
  6. Exercise:
     1. Modify the parameters of a K-NN model and evaluate the changing performance.
     2. Use Scikit Learn to build a predictive model using a different algorithm.
• Kaggle Challenge Presentations
  1. Give a brief presentation about your work, results, and what you learned.

Thinking about starting a boot camp or a new field and unsure of your background or level of technical skills? We’ve got you covered. Join our Data Analysis with Python Mini Bootcamp where you’ll learn the necessary prerequisite skills while learning basic data science. This boot camp is for non-coders who are interested in learning data science or those who have a technical background although are not familiar with the fundamentals of data science.

Data Analysis with Python Mini Bootcamp will teach you the fundamental skills of Python, SQL, Statistics and an introduction to machine learning. With those skills, you will be ready to take any kind of data science boot-camp or you’ll be ready to start learning data science on your own. During our prep course you will explore a hands-on experience that will ignite your enthusiasm and confidence to learn data science and its’ theory.

Curriculum

• Week 1 :
  1. Review Python common functionalities and data structures used in data science
  2. Learn the important Python libraries in data science (Pandas, Numpy, Matplotlib)
• Week 2:
  1. Read and write data from/to different formats (excel, csv, text, etc.)
  2. Cleanse and select important records from Dataframes
  3. Deal with missing data: identify, replace, and eliminate records
  4. Sort Dataframes by multiple columns
• Week 3:
  1. Leverage the functions apply, lambda, filter, and map
  2. Merge/Join Dataframes by foreign keys
  3. Learn pivot tables in Pandas
• Week 4:
  1. Learn data visualizations with the libraries Matplotlib and Seaborn
  2. Introduction to the Machine Learning library Sklearn
  3. Apply linear and logistic regression with Sklearn

The role of a data scientist is to turn raw data into actionable insights. To be an effective data scientist, you must know how to extract data from these databases using SQL. In this course you’ll learn the skills you need to extract critical insights in a database.

Who is this course for:

• Anyone who wants to start a data analyst or data scientist role in professional life.
• Anyone interested in expanding data skills.
• Anyone who wants to start learning data science.

Curriculum

• DB Basics
• Subqueries
• Aggregations
• Joins
• Rollups and Cubes
• Window Functions
• Transposing and Ranking Data
• Hierarchical Queries
• Analytical SQL Functions

Today, there’s a huge demand for data science expertise as more and more businesses apply it within their operations. Python offers the right mix of power, versatility, and support from its community to lead the way. While Python is most popular for data wrangling, visualization, general machine learning, deep learning and associated linear algebra (tensor and matrix operations), and web integration, its statistical modeling abilities are far less advertised. A large percentage of data scientists still use other special statistical languages such as R, MATLAB, or SAS over Python for their modeling and analysis. However, only by Python-based statistical modeling, one can build a powerful end-to-end data science pipeline (a complete flow extending from data acquisition to final business decision generation) using a single programming language. This bootcamp will teach fundamentals of statistical modeling concepts with easy-to-follow examples in Python to get you started in your data science journey.

Curriculum

• Descriptive statistics
  1. A bit of history, why statistics is ‘hot’ today, a few examples
  2. Central tendency, dispersion measures, other simple descriptive measures
  3. Bi-variate statistics, correlation, plotting
  4. Probability, random variables, probability distributions
  5. Exploratory data analysis (EDA) example using descriptive stats
• Inferential statistics
  1. Estimation, inference, confidence interval
  2. Hypothesis testing, p-values, statistical significance
  3. Type-I/Type-II errors, nature of statistical learning, difference from ML
     Comparing means, t-test, ANOVA
• Application in machine learning/data science
  1. Linear regression, posing linear regression as statistical inference problem
  2. Logistic regression, inference
  3. Naive Bayes classification, maximum likelihood estimation
  4. K-means clustering, expectation-maximization (E-M), Gaussian mixture model (GMM)

R is now considered one of the most popular analytics tools in the world. This Machine Learning with R mini bootcamp dives into the basics of machine learning using this approachable, and well-known, programming language. During this mini bootcamp, You’ll learn about Supervised vs Unsupervised Learning, and you will also look into Dimensionality Reduction & Collaborative Filtering.

This mini bootcamp also provides you to look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

Who is this course for:

• Anyone who wants an introduction to Machine Learning with R.
• Anyone who is interested in building ML applications in R.

Course Requirements:

• Need to know theory of ML.
• Basic Math knowledge.

Curriculum

• Module 1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
  1. Machine Learning Languages, Types, and Examples
  2. Machine Learning vs Statistical Modeling
  3. Supervised vs Unsupervised Learning
  4. Supervised Learning Classification
  5. Unsupervised Learning
• Module 2 – Supervised Learning I
  1. K-Nearest Neighbors
  2. Decision Trees
  3. Random Forests
  4. Reliability of Random Forests
  5. Advantages & Disadvantages of Decision Trees
• Module 3 – Supervised Learning II
  1. Regression Algorithms
  2. Model Evaluation
  3. Model Evaluation: Overfitting & Underfitting
  4. Understanding Different Evaluation Models
• Module 4 – Unsupervised Learning
  1. K-Means Clustering plus Advantages & Disadvantages
  2. Hierarchical Clustering plus Advantages & Disadvantages
  3. Measuring the Distances Between Clusters – Single Linkage Clustering
  4. Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
  5. Density-Based Clustering
• Module 5 – Dimensionality Reduction & Collaborative Filtering
  1. Dimensionality Reduction: Feature Extraction & Selection
  2. Collaborative Filtering & Its Challenges

Time series forecasting is an important part of machine learning that requires an additional effort to recognize the impact of the time-component of the problems, such as trends and seasonality. In this series of lectures, you will be introduced to basics of time series models in Python environment. Specifically, you will have gain hands-on experience on handling forecast models in four sessions.

Who is this course for:

• Anyone who wants to be master in time series data with python
• Anyone who wants to become proficient in time series data analysis working with real life data
• People interested in applying machine learning techniques to time series data

Course Requirements:

• Prior familiarity with the interface of Jupiter Notebooks
• Prior exposure to basic statistical techniques
• Be able to carry out data reading and pre-processing tasks such as data cleaning In python

Curriculum

• Week 1. Fundamentals
  1. Python Environment
  2. What is Time Series Forecasting?
  3. Time Series as Supervised Learning
  4. Load and Explore Time Series Data
  5. Data Visualization
  6. Resampling and Interpolation
  7. Power Transforms
  8. Moving Average Smoothing
• Week 2. Temporal Structure
  1. Introduction to White Noise
  2. Introduction to the Random Walk
  3. Decompose Time Series Data
  4. Use and Remove Trends
  5. Use and Remove Seasonality
  6. Stationarity in Time Series Data
• Week 3. Evaluate Models
  1. Backtest Forecast Models
  2. Forecasting Performance Measures
  3. Persistence Model for Forecasting
  4. Visualize Residual Forecast Errors
  5. Reframe Time Series Forecasting Problems
• Week 4. Forecast Models
  1. Introduction to the Box-Jenkins Method
  2. Autoregression Models for Forecasting
  3. Moving Average Models for Forecasting
  4. ARIMA Model for Forecasting
  5. Autocorrelation and Partial Autocorrelation
  6. Grid Search ARIMA Model Hyperparameters
  7. Save Models and Make Predictions
  8. Forecast Confidence Intervals

This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you an explanation of the main tools in NLP such as: Working with Test Data in Python, Text processing with NLTK and Spacy, Text Normalization, Model Building and finally developing NLP projects.

Who is this course for:

• Anyone who is comfortable writing Python code, using loops, lists, dictionaries, etc.
• Anyone who want to learn more about machine learning
• Anyone with interest in Natural Language Processing and it’s applications

Course Requirements:

• Basic knowledge in python coding
• Basic knowledge in Machine Learning
• Basic knowledge in linear algebra and probability

Curriculum

• Week 1:
  1. Introduction to the course and expectations
     1. What is NLP?
     2. Examples of NLP in the real world
     3. What to expect from this course?
     4. NLP Workflow
  2. Basics of language processing & understanding text
     1. Regular Expressions for text processing
     2. Scraping Websites
     3. NLTK and spaCy
     4. Tokens, Lemmas
• Week 2:
  1. Supervised Learning Models for Text Data
     1. Bag of Words with Classification Models
• Week 3:
  1. Unsupervised Learning Models for Text Data
     1. TFIDF
     2. Latent Semantic Analysis
     3. Dimension Reduction
  2. Unsupervised neural network
     1. Introduction to word2vec
• Week 4:
  1. Build your own NLP Model
     1. A hands-on project
  2. An introduction to Advanced NLP topics

Learn how to build, train and apply fully connected neural networks.

Curriculum

Explore deep learning, frameworks, and strategies for model deployment.

Curriculum