Take Your Analytics to the Next Level
Learn how to process and analyze data, build innovative data products, and use machine learning and data engineering methods to streamline your operations. Our data science training will equip you and your team with the analytical and technical skills you need to work more efficiently, produce better results, and arrive at better data-driven decisions.
Data Science and Analytics Courses
Data Literacy for Managers & Executives
Learn the foundations of the analytics workflow, including problem identification and framing, analytical methods and tools, understanding visualizations, crafting compelling stories using data, hiring analytical talent, and considerations for data-driven decision making.
Intro to Data Science with Python
Learn how to analyze and extract insights from data using one of the most popular tools for data analysis today. Gain hands-on experience programming with Python and familiarity with Python's syntax, data structures, control flow, and data analysis libraries.
Building Data Apps with Python
Learn how to structure a data product using every stage of the data science pipeline in this project-based, hands-on course. Gain experience ingesting data from the web, wrangling data into a structured database, and building a data-driven application.
R Statistical Programming
Learn how to import, manipulate, and analyze data with R - a powerful, open source statistical programming language. Gain hands-on experience programming with R in addition to performing data exploration, visualization, and machine learning on real-world data sets.
Data Engineering with Python
Learn how to ingest, clean, model, and store large volumes of data acquired from the web. Develop the ability to create and manage big data pipelines, set up analytics infrastructure, and learn how to select the right technologies for the problem at hand.
Microsoft Power BI
Gain hands-on experience creating reports, visualizations, and dashboards with Microsoft Power BI. Learn how to connect to different data sources, model your data, create custom visualizations and dashboards, and share and collaborate on reporting.
Machine Learning with Python
Learn about supervised and unsupervised machine learning methods for regression, classification, and clustering. Gain experience implementing different types of algorithms, feature engineering, training and evaluating models, and building machine learning pipelines.
Visual Analytics With Python
Gain an understanding of how to perform visual exploration, reporting, and storytelling using Python’s data visualization libraries. Learn visual diagnostics that can be incorporated into the machine learning workflow for more informed feature selection and modeling.
Advanced Analysis with Excel
Go beyond the basics and master data analysis with Excel and Power BI. Learn how to clean and manipulate raw data, aggregate and filter to get at the exact information you need, model real world scenarios, and create visually appealing reports and dashboards.
Big Data Analytics and Machine Learning
Learn about the opportunities and challenges that come with analyzing very large data sets. Gain a comprehensive overview of the Hadoop ecosystem as well as hands-on experience analyzing data and performing machine learning with MapReduce, Spark, and other big data technologies.
NLP & Text Analytics with python
Learn how to computationally read, preprocess, and analyze text data using Python libraries like NLTK, gensim, spacy, and more. Gain hands-on experience with corpus-building, tokenization, part-of-speech tagging, information extraction, topic modeling, text classification, and document clustering.
Network Analysis With Python
Learn how to identify connections between entities and analyze the relationships those connections represent. Gain experience traversing and querying graphs, clustering to identify communities, creating network visualizations, and generating predictions from network data.
Any of our courses can be tailored to meet the specific needs of your organization. Completely customized courses are also available.
Our Clients
We have successfully taught hundreds of people from dozens of organizations how to use modern methods to analyze the data they work with, discover insights, and develop innovative data products. Below are just a few of the organizations we have worked with.
The Best Data Science & Analytics Training
All our instructors have several years of experience in industry in addition to teaching experience and are knowledgeable about all aspects of data science, analytics, machine learning, and big data. To get a free customized training quote, click the button below.
Corporate Training Case Studies
Below are a few case studies on training solutions we have provided for clients.
Teaching The U.S. State Department
We currently teach 4 courses (Data Literacy for Managers, Data Literacy for Executives, Advanced Analysis with Excel & Power BI, and Intro to R Programming) several times per year for this client.
In the Data Literacy classes, participants learn about analytics workflows and methods, cognitive biases, tools and techniques, how to interpret data, and limitations and ethical issues surrounding data analysis.
In the Advanced Excel & Power BI class, participants learn how to clean and manipulate raw data, apply analytic techniques to visualize complex data sets, solve optimization problems, and automate their analyses.
In the Intro to R Programming class, participants learn how to import, manipulate, and analyze data and how to perform data exploration, visualization, and modeling using the R statistical programming language.
Giving Freddie Mac Big Data Capabilities
District Data Labs was hired to teach two courses to over 150 of the client’s employees. Both courses were structured similarly - content was organized into half-day sessions and taught 4 days per week for two weeks per cohort.
District Data Labs successfully taught 5 cohorts of the Machine Learning with Python course and 3 cohorts of the Big Data Machine Learning & Analytics course.
In the Machine Learning with Python course, participants gained deep knowledge about tree-based machine learning algorithms and how to build and implement them with Python.
In the Big Data Machine Learning & Analytics course, participants were taught about the challenges that arise when processing, analyzing, and performing machine learning on very large data sets and how to address those with distributed computing platforms like Hadoop and Spark.
U.S. Department of Defense Calls District
District Data Labs provided the U.S. Department of Defense with multiple on-site training sessions. The first of these was a two-day course covering concepts and practical applications of data science, machine learning, and big data.
Day 1 topics included data science workflows, data acquisition, data storage, data wrangling, and entity resolution. Day 2 topics included machine learning architectures, model types, model performance evaluation methods, and an introduction to distributed computing for large data sets.
District Data Labs also taught a 2-day Data Wrangling with Python class two other times for the client. Topics for this course included an introduction to Python, data acquisition via web scraping, data storage options, data manipulation and analysis, and creating visualizations.
Helping The Federal Reserve Board Wrangle Data
District Data Labs provided training on Python programming, data wrangling, and natural language processing to this client. The course consisted of three full-day sessions.
Session 1 covered Python data types, control flow, and functions. Session 2 covered importing libraries, and data ingestion and wrangling with the pandas library. Session 3 covered an introduction to the natural language toolkit (NLTK) library, text preprocessing and corpus-building, and topic modeling and analysis.
This was the second training District Data Labs had provided to the client. The previous training consisted of a two-day Introduction to Big Data course that covered distributed computing methods, the Hadoop ecosystem, and leveraging Spark for fast data analytics.
Brookings Institution Gets Analytical
District Data Labs provided the client with on-site training on text analytics methods. The training consisted of three half-day sessions covering methods for conducting analysis and machine learning on text data.
Session 1 covered topics such as corpus creation, tokenization, statistical analysis, stemming and lemmatization, and n-gram analysis. Session 2 covered tagging, syntactic parsing, chunking, key phrase extraction, and named entity recognition. Session 3 covered text vectorization and machine learning methods for classification and regression.
District Data Labs had conducted training for this client previously: a half-day session on web scraping and crawling and another half-day session on SQL integration with Python, R, and Stata.