Data Science with Python Training in Washington, DC, United States

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analyzing, extracting information and making informed business decisions through big data.

This Data Science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.

Extensive hands-on labs and interview prep will help you land lucrative jobs.

What You Will Learn


There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

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Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study


  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 


Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA


  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree


  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data. Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • Case Study: Time Series Modeling on Stock Price


  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor


 Project to be selected by candidates.


Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science?

If there is a job that is in demand in the 21st century, it is that of a Data Scientist. User data is highly valuable these days, with major companies like Facebook and Google selling them to companies for advertisement purposes. As a result, companies know what you like and what you don’t. Accordingly, they recommend you products, even if you haven’t enquired about it in the first place. 

It is clear that Data Science is in high demand in Washington right now. Companies like Amazon Web Services, Booz Allen Hamilton, People (Technology and Processes), Addx Corporation, CGI Group, Inc., Central Intelligence Agency, Salient CRGT, York and Whiting, etc. are hiring data scientists right now at a handsome pay.

Other reasons behind the popularity of data science include:

  • There is an increase in the demand for data-driven decisions.
  • Data scientists are the highest paid professionals in the tech field.
  • The high rate of data collection means there is a need for faster data analysis for getting the most out of data, which is the expertise of data scientists.

Washington is home to several universities that offer Data Science programs including Bellevue College, City University of Seattle, Seattle University, University of Washington, etc. These courses will help you acquire the technical skills required to make it big in the field of Data Science.

Some of the top skills required for becoming a data scientist in Washington include:

  • Python Programming: In the data science field, Python is one of the most commonly used programming languages. It is a simple and versatile language that allows processing of different formats of data. Data scientists can create datasets with Python and perform operations on them
  • R: Data scientists are required to have a good understanding of analytical tools like R programming for solving data science problems. A knowledge of R is always beneficial in the field of data science
  • Hadoop Platform: Knowledge of Hadoop platform isn’t a strict requirement in the field; however, it is still heavily preferable. It is a skill that is highly valued for the job.
  • SQL: SQL allows data scientists access, communicate and work on data. With its help, data scientist can understand the structure and formation of database. MySQL has concise commands which saves time and reduces technical requirement for performing database operations.
  • Machine Learning and AI: In the field of data science, proficiency in Machine Learning and AI is very much a prerequisite for a career. Certain  concepts that you need to be familiar with include:
    • Neural Networks
    • Decision trees
    • Reinforcement Learning
    • Adversarial learning
    • Logistic regression
    • Machine Learning algorithms
  • Apache Spark: Apache Spark is similar to Hadoop in the sense that it is a big data computation. It is faster than Hadoop because it makes use of caches of its computation in system memory instead of reading and writing to the disk. Therefore, with the help of Apache Spark, data science algorithms can run faster. It is also helpful while handling large and complex unstructured datasets. It also prevents loss of data and operates at high speed. Data scientists can carry out projects using Apache Spark with much ease.
  • Data Visualization: Data scientists are expected to be able to visualize data with the help of tools like Tableau, d3.js, matplotlib and ggplot. With the help of these tools, data scientists can convert complex results from data set processing into an easily understandable format. Through data visualization, organizations can directly work on data. It also helps data scientists gain insights from data and its outcomes.
  • Unstructured Data: A data scientist should know how to work with unstructured data, which has both unlabelled and unorganized content. Examples of such data include audio, video, social media posts, blog posts, etc.

Some behavioural traits a data science professional should have include:

  • Curiosity: You need to have the curiosity and eagerness to learn what it takes to deal with massive data on a regular basis.
  • Clarity: If you are someone who is constantly confused with questions, data science isn’t the field for you. In data science, you need to have clarity while doing anything, be it writing codes or cleaning up data.
  • Creativity: From visualizing data to developing new tools, you need to be creative to become successful in data science.
  • Scepticism: The difference between a data scientist and a normal creative mind is the presence of scepticism to stay grounded in the real world.

The corporations that are employing Data Scientists in Washington,DC include Advanced Decision Vectors, Optimal Solutions Group, The Buffalo group, Teracore, Bixal, Big League Advance, Atlas Research, Gallup, Penn Schoen Berland, Cerebri AI, The Rock Creek Group, etc.

Given the popularity of the job, there are plenty of benefits of being a data scientist, including:

  1. High Salary: Everyone likes being paid well, particularly if the job has such high requirements. With the increasing demand of data scientists, the salary for the job is one of the highest in the industry.
  2. Great bonuses: Impressive bonuses and other perks can also be expected
  3. Education: The field of data science requires you to have at least a Master’s degree to be a data scientist. You may even get opportunities to work as a researcher or a lecturer.
  4. Mobility: A lot of businesses collecting data are situated in developed countries, where the standard of living is high. Getting hired by these businesses will give you lucrative pay packages too.
  5. Building a network: The more involved you are, the bigger your network of data scientists would be.

Data Scientist Skills & Qualifications

The top business skills required for becoming a data scientist include:

  1. Analytical Skills: Understanding and analysis of the problem is important in order to find the right solution. For that, clarity and strategy awareness is necessary.
  2. Communicative skills: A data scientist has the responsibility to communicate deep business or customer analytics to companies.
  3. Intellectual curiosity: A certain level of curiosity is necessary in the field of data science. It's essential in finding results that deliver value to businesses.
  4. Knowledge of the industry: Lastly, this is one of the most vital skills. Having a good industry knowledge provides a clear idea about what should be paid attention to.

The following ways can help you brush up your skills in data science:

  • Bootcamps: Lasting over 4-5 days, bootcamps help improve theoretical knowledge while also gaining valuable hands-on experience. There are several boot camps organized in Washington,DC that will help you brush up your skills.
  • MOOC Course: Online courses can be taken on the latest industry trends. Taught by experts in the field, MOOC courses have assignments for implementation as well.
  • Certifications: With certifications, you improve both your CV and your skill set. Below are some of the popular data science certifications:
    • Applied AI with Deep Learning
    • IBM Watson IoT Data Science Certificate
    • Cloudera Certified Professional: CCP Data Engineer
    • Cloudera Certified Associate - Data Analyst
  • Projects: Projects are a great way to refine your skills by exploring solutions to questions in different ways.
  • Competitions: There are competitions such as Kaggle, which help in improving skills in problem solving.

Washington,DC is a hub to several major and small corporations that use Data Science for optimizing their business processes and making crucial marketing decisions. These corporations include Advanced Decision Vectors, Optimal Solutions Group, The Buffalo group, Teracore, Bixal, Big League Advance, Atlas Research, Gallup, Penn Schoen Berland, Cerebri AI, The Rock Creek Group, Amazon Web Services, Booz Allen Hamilton, People (Technology and Processes), Addx Corporation, CGI Group, Inc., Central Intelligence Agency, Salient CRGT, York and Whiting, etc.

Practicing is one of the best ways to gain a mastery of Data Science. You can practice by working on the data science problems given below, as per the level of expertise:

  • Beginner Level:
    • Iris Data Set: In the pattern recognition field, the Iris Data Set is considered as the easiest, most versatile and resourceful data set that can be incorporated while learning different classification techniques. It contains only 50 rows and 4 columns.Problem to Practice: Predicting the class of a flower depending on the parameters.
    • Bigmart Sales Data Set: The Retail Sector uses analytics heavily for optimizing business processes. Business Analytics and Data Science allow efficient handling of operations. The data set contains 8523 rows and 12 variables and is used in Regression problems.Problem to Practice: Predicting a retail store’s sales
    • Loan Prediction Data Set: As compared to all other industries, the banking field uses data science and analytics most significantly. This data set can help a learner by providing an idea of the concepts in the field of insurance and banking, along with the strategies, challenges and variables influencing outcomes. It contains 615 rows and 13 columns.Problem to Practice: Predicting whether a given loan would be approved by the bank or not.
  • Intermediate Level:
    • Black Friday Data Set: This Data Set consists of retail store’s sales transaction and it can be used for exploring and expanding engineering skills. It is a regression problem and contains 550,609 rows and 12 columns.Problem to practice: Predicting the total purchase amount
    • Text Mining Data Set: This data set contains safety reports describing problems encountered on flights. It is a multi-classification and high-dimensional problem and contains 21,519 columns and 30,438 rows.Problem to practice: Classifying documents depending on their labels.
    • Human Activity Recognition Data Set: This Data Set consists of 30 human subjects collected through smartphone recordings. It consists of 10,299 rows and 561 columns.Problem to practice: Predicting the category of human activity.
  • Advanced Level:
    • Urban Sound Classification: Most beginner Machine Learning problems do not deal with real world scenarios. The Urban Sound Classification introduces ML concepts for implementing solutions to real world problems. The data set contains 8,732 urban sound clipping classified in 10 categories. The problem introduces concept of real-world audio processing.Problem to practice: Classifying the type of sound from a specific audio.
    • Identify the digits: This data set contains 31 MB of 7000 images in total, each having 28x28 dimension. It promotes study, analysis and recognition of image elements.Problem to practice: Identifying the digits in an image.
    • Vox Celebrity Data Set: Audio processing is an important field in the domain of Deep Learning. This data set contains words spoken by celebrities and is used for speaker identification on a large scale. It consists of 100,000 words from 1,251 celebrities from across the world.Problem to practice: Identifying the celebrity based on a given voice.

How to Become a Data Scientist in Washington, DC

Given below are the steps needed to become top data scientist:

  1. Select an appropriate programming language to begin with. R and Python are usually recommended
  2. Dealing with data involves making patterns and finding relationship between data. A good knowledge of statistics and basic algebra is a must
  3. Learning data visualization is one of the most crucial steps. You need to learn to make data as simple as possible for the non-technical audience
  4. Having the necessary skills in Machine Learning and Deep Learning is necessary for all data scientists.

To prepare for a data science career, you need to follow the given steps and incorporate the appropriate skills:

  1. Certification: You can start with a fundamental course to cover the basics. Thereafter, you can grow your career by learning application of modern tools. Also, most Data Scientists have Ph.Ds, so you would also be needed to have enough qualification.
  2. Unstructured data: Raw data is not used in the database as it is unstructured. Data scientists have to understand the data and manipulate it to make it structured and useful.
  3. Frameworks and Software: Data scientists need to know how to use the major frameworks and software along with appropriate programming language.
    • R programming is preferred because it is widely used for solving statistical programs. Even though it has a steep learning curve, 43% data scientists use R for data analysis.
    • When the amount of data is much more than the available memory, a framework like Hadoop and Spark is used.
    • Apart from the knowledge of framework and programming language, having an understanding of databases is required as well. Data scientists should know SQL queries well enough.
  4. Deep Learning and Machine Learning: Deep Learning is used to deal with data that has been gathered and prepared for better analysis.
  5. Data Visualization: Data Scientists have the responsibility of helping business take informed decisions through analysis and visualization of data. Tools like ggplot2, matplotlib, etc. can be used to make sense of huge amounts of data.

Washington,DC is home to several universities that offer a degree in Data Science programs including Bellevue College, City University of Seattle, Seattle University, University of Washington, etc. The importance of degree in the field is summarized below:

  • Networking: Networking is important in all fields and it can be developed while pursuing degrees.
  • Structured education: Having a structured curriculum and a schedule to follow is always beneficial.
  • Internships: These provide that much needed practical experience
  • Qualification for CVs: Earning a degree from a reputed institution is always helpful for your career.

If you are looking for a master's degree in Data Science, Washington,DC has a lot to offer. There are many leading universities offering Data Science programs, such as Bellevue College, City University of Seattle, Seattle University, University of Washington, etc. But first, you need to figure out if you even need a degree or not. The given scorecard can help you determine whether you should get a Master’s degree. You should pursue the degree if you get over 6 points in total:

  • A strong background in STEM (Science/Technology/Engineering/Management)- 0 point.
  • Weak STEM background, such as biochemistry, biology, economics, etc.- 2 points
  • Non-STEM background- 5 points
  • Python programming experience less than 1 year in total- 3 points
  • No job experience in coding- 3 points
  • Lack of capability to learn independently- 4 points
  • Not understanding that this scorecard follows a regression algorithm- 1 point.

Programming knowledge is a must for any aspiring data scientist because:

  • Analysing data sets: Programming helps data scientists to analyse large amounts of data sets
  • Statistics: The knowledge of statistics is not enough. Knowing programming is required to implement the statistical knowledge.
  • Framework: The ability to code allows data scientists to efficiently perform data science operations. It also allows them to build frameworks that organizations can use for visualizing data, analysing experiments and managing data pipeline.

Data Scientist Salary in Washington, D.C.

In Washington, a Data Scientist can earn up to $122,328 per year.

In Washington, the average salary of a data scientist is $122,328 as compared to $110,925 in Chicago.

The average income of a data scientist in Washington is $122,328 as compared to $125,310 in Boston.

A data scientist earns an average of about $122,328 every year in Washington as compared to $128,623 in New York.

If you are a Data Scientist in Washington, you can expect an average annual salary of $122,328. There are no other cities in District of Columbia.

There is a huge demand for Data Scientists in Washington. There are a number of job listings in various portals offering handsome salaries and perks to Data Scientists. And this number is not going to go down anytime soon.

The benefits of being a Data Scientist in Washington are that there are multiple job opportunities and the pay is good. Also, you can get a chance to work with major brands, such as InfoStrat, 3Pillar Global, etc. 

The perks and advantages of being a Data Scientist in Washington is the opportunity it allows to network and connect with other data scientists. This not only benefits the data science community but also gets you a chance to network with major data scientists. Also, Data Scientists have the luxury to choose a field of their interest. They get to work with the latest technology with enormous potential. Data Scientists can easily get in the eyes of the top-level executives as they have a key role in providing useful business insights after analyzing the data.

The top companies hiring Data Scientists in Washington are cBEYONData, Trianz, DataLab USA, PieSoft, CapTech, Kroll, Covalense, InfoStrat, 3Pillar Global, CloverDX, DecisionPath Consulting, Akira Technologies, Cogent Communications, etc.

Data Science Conferences in Washington, D.C.

S.NoConference nameDateVenue
1.2019 Dataworks Summit20-23 May, 2019Marriott Marquis Washington, DC, Massachusetts Avenue Northwest, Washington, DC, USA
2.AI World | Government Conference and Expo24-26 June, 2019

Ronald Reagan Building and International Trade Center 1300 Pennsylvania Ave NW Washington, DC 20004

3.Data-Driven Government25 September, 2019Capital Hilton 16th & K Street, NW Washington, DC 20036
4.Chief [Data] Analytics Officers & Influencers29-30, May 2019The Embassy Suites by Hilton Washington DC Convention Center, 900 10th Street NW, Washington, District of Columbia, 20001, USA
5.Subsurface Data and Machine LearningJune 6, 2019

National Academy of Sciences 2101 Constitution Ave NW Room 125 Washington, DC 20001, United States

1. 2019 Dataworks Summit, Washington

  • About the conference: The conference will cover the AI and data science technologies, like Apache Zeppelin, PyTorch, DL4J, TensorFlow, etc. and explore new opportunities in predictive analytics, process automation, and decision optimization.
  • Event Date: 20-23 May, 2019
  • Venue: Marriott Marquis Washington, DC, Massachusetts Avenue Northwest, Washington, DC, USA
  • Days of Program: 4
  • Timings: May 20: 8:30 AM - 5:00 PM
    • May 21: 8:30 AM - 8:30 PM
    • May 22: 8:00 AM - 6:00 PM
    • May 23: 8 AM - 5:30 PM
  • Purpose: The conference aims to cover the entire lifecycle of data science, that is, development, test, and production, by learning and exploring various examples of analytics applications and systems.
  • Speakers & Profile:
    • Cathy O'neil - New York Times Bestselling Author, Data Scientist, and Mathematician
    • Hilary Mason - General Manager, Machine Learning, Cloudera
    • Charles Boicey - Chief Innovation Officer, Clearsense LLC
    • Nick Psaki - Principal, Office of the CTO, Pure Storage Federal, Pure Storage
    • Mick Hollison - Chief Marketing Officer, Cloudera
    • Jerry Green - WW Open Source Sales and Strategy Leader, IBM
    • Barbara Eckman - Senior Principal Software Architect, Comcast
    • Alex Yang - CTO and Chief Architect, IBM China Development Laboratory
    • Kamil Bajda-Pawlikowski - CTO & co-founder, Starburst
    • Pradeep Bhadani- Senior Big Data Engineer,
    • Owen O'malley - Co-founder & Technical Fellow, Cloudera
  • Who are the major sponsors:
    • Hortonworks
    • IBM
    • Pure Storage
    • HP Enterprise
    • Syncsort
    • Attunity
    • Dremio
    • Wandisco
    • Tiger Graph

2. AI World | Government Conference and Expo, Washington

  • About the conference: This conference will help its attendees improve their professional performance by exploring the latest innovations in AI and intelligent automation, and its application in different sectors.
  • Event Date: 24-26 June, 2019
  • Venue: Ronald Reagan Building and International Trade Center, 1300 Pennsylvania Ave NW, Washington, DC 20004
  • Days of Program: 3
  • Purpose: The conference brings together Data experts from various areas to dive deep into intelligent automation technology, AI, and discuss best practices to identify the challenges in these areas including business, government, technology, and civil society, and explore solutions to it.
  • Speakers & Profile:
    • Robert Ames, Senior Director, National Technology Strategy, VMware Research, VMware
    • Ian Beaver, Ph.D., Lead Research Engineer, Intelligent Self Service, Verint
    • David Bottom, CIO, Intelligence and Analysis Office, US Department of Homeland Security
    • David Bray, Ph.D., Executive Director, People-Centered Internet Coalition; Senior Fellow, Institute for Human-Machine Cognition
    • Alison Brooks, Ph.D., Research Director, Smart Cities Strategies & Public Safety, IDC
    • Rich Brown, Director, Project VIC International
    • Jeff Butler, Director of Data Management, IRS
    • Dan Chenok, Executive Director, IBM Center for The Business of Government, IBM
    • Sung-Woo Cho, Ph.D., Senior Associate/Scientist, Social and Economic Policy, Abt Associates
    • Nazli Choucri, Ph.D., Professor of Political Science, MIT
    • Ruthbea Clarke, Vice President, IDC Government Insights, IDC
    • Lord Tim Clement-Jones CBE, Former Chair of the UK’s House of Lords Select Committee for Artificial Intelligence and Chair of Council, Queen Mary University of London
    • Michael Conlin, Chief Data Officer, U.S. Department of Defense 
    • Thomas Creely, Ph.D., Director, Ethics and Emerging Military Technology Graduate Program, U.S. Naval War College
    • Daniel Crichton, Program Manager, Principal Investigator, and Principal Computer Scientist, NASA’s Jet Propulsion Laboratory 
    • Chris Devaney, Chief Operating Officer Executive - Business Operations, DataRobot
    • Michael Dukakis, Chairman, Boston Global Forum
    • Justin Fier, Director of Cyber Intelligence and Analytics, Darktrace
    • Diana Furchtgott-Roth, Deputy Assistant Secretary for Research and Technology, U.S. Department of Transportation
    • Arti Garg, Director, Emerging Markets & Technology, AI, Cray, Inc.
    • Sabine Gerdon, Fellow, AI and Machine Learning, World Economic Forum, Centre for the Fourth Industrial Revolution, World Economic Forum, Centre for the Fourth Industrial Revolution
    • Rob Gourley, Co-Founder, and CTO, OODA LLC
  • Whom can you Network with in this Conference:
    • Central and Federal Government Officials and Staff
    • State and Local Agency Leadership
    • Government Solutions Providers
    • Academic / Research
    • Service and Humanitarian Organizations
    • Research and Media
  • Registration cost: 
    • Three Day VIP All Access - Monday to Wednesday, June 24-26
      • * Advance - Registration Rate Until June 7, 2019
        • Government/Academic : $599
        • Commercial: $1,399
      • * Standard Registration and On-Site
        • Advance - Registration Rate Until June 7, 2019
        • Government/Academic : $799
        • Commercial: $1,599
    • Conference Only - Tuesday to Wednesday, June 25-26
      •  * Advance - Registration Rate Until June 7, 2019
        • Government/Academic : $499
        • Commercial: $1,099
      • *  Standard Registration and On-Site
        • Advance - Registration Rate Until June 7, 2019
        • Government/Academic : $699
        • Commercial: $1,299
  • Who are the major sponsors:
    • International Data Corporation (IDC)
    • The Michael Dukakis Institute for Leadership and Innovation (MDI)
    • VMware 
    • Cray 
    • Darktrace 
    • DataRobot 
    • eGlobalTech
    • NCI 
    • UiPath 
    • Pure Storage

3. Data-Driven Government, Washington

    • About the conference: The conference is held to enhance the deployment of machine learning and analytics across government agencies.
    • Event Date: 25 September, 2019
    • Venue: Capital Hilton 6th & K Street, NW Washington, DC 20036
    • Days of Program: 1
    • Purpose: The purpose of the conference is to cover the current day’s application of AI and analytics across government bodies and software vendors. 
    • Speakers & Profile:
      • Government Employee Pass
        • 1 Day Conference Pass: $345
        • 2 Day Conference Pass (Conference + Workshop): $895
        • 3 Day Conference Pass – (Conference + 2 Workshops):    $1,455
        • Workshops Only: $600
      • Private Industry / Contractors
        • 1 Day Conference Pass: $595
        • 2 Day Conference Pass (Conference + Workshop): $1,695
        • 3 Day Conference Pass – (Conference + 2 Workshops):    $2,795
        • Workshops Only: $1,200
  • Who are the major sponsors:

    • Deloitte
    • Google Cloud
    • DataRobot
    • IBM
    • Elder Research
    • Sas
    • Alteryx
    • Neo4j
    • ESRI

4. Chief [Data] Analytics Officers & Influencers, Washington

  • About the conference: The conference allows its attendees to achieve their goals effectively by connecting them to technologies, insights, and people.
  • Event Date: 29-30 May, 2019
  • Venue: The Embassy Suites by Hilton Washington DC Convention Center, 900 10th Street NW, Washington, District of Columbia, 20001, USA
  • Days of Program: 2
  • Timings: 8 A.M. to 6 P.M.
  • Purpose: This conference aims to connect experts and leaders from the field of the data industry and to impart knowledge on best practices, latest innovations, and challenges.
  • Speakers & Profile:
    • Donna Roy - Interim Chief Data Officer, U.S. Department of Homeland Security
    • Daniel Ahn - Chief Economist and Head of Data Analytics, U.S. Department of State
    • Caryl Brzymialkiewicz - Chief Data Officer, U.S. Department of Health and Human Services Office of Inspector General
    • John Bergin - Deputy Assistant Secretary for Army (Financial Information Management), United States Army
    • Jon Minkoff - Chief Data Officer, Enforcement Bureau, Federal Communications Commission
    • Vasil Jaiani - Chief Performance Officer and Chief Data Officer, Department of Public Works
    • Tammy Tippie - Chief Data Scientist, Office of the Chief of Naval Operations
    • Robert Toguchi - Chief, Concepts Division, US Army Special Operations Command (SOCOM)
    • Dr. Robert Whetsel - Chief Technical Advisor to the 4th Estate, Department of Defense
    • Jennifer Lambert - Acting Director, Centre for Analytics, U.S. Department of State
    • Jim Rolfes - Chief Information Officer, U.S. Consumer Product Safety Commission
    • Rosa Akhtarkhavari - Chief Information Officer, City of Orlando
  • Registration cost:
    • Government Data & Analytics Practitioners: FREE
    • Non-Government Data & Analytics Practitioners: $999
    •  Vendor / Solution Providers: $2,999

    5. Subsurface Data and Machine Learning, Washington

    • About the conference: The conference is organized by the Committee on Earth Resources, to develop data analytics to develop new opportunities for analysis and collection of data on the contents of Earth’s subsurface.
    • Event Date: June 6, 2019
    • Venue: National Academy of Sciences 2101 Constitution Ave NW Room 125 Washington, DC 20001, United States
    • Days of Program: 1
    • Timings: 10:00 AM – 4:30 PM EDT
    • Purpose: The purpose of the conference is to develop advanced data analyses like machine learning and AI to enhance scientific and public understanding of subsurface including energy, water resources, and environmental hazards.
    S.NoConference nameDateVenue
    1.The Washington Big Data Conference 201702/10/2017Walter E. Washington Convention Center, 801 Mt Vernon Pl NW, Washington, DC 20001, USA

    1. The Washington Big Data Conference 2017

    • Conference City: Washington, USA
    • About: The conference was headed by professionals from the backgrounds of IT, Digital Analytics, Analytics, (Master) Data Management, Predictive Analytics, and Big Data.
    • Event Date: 02/10/2017
    • Venue: Walter E. Washington Convention Center, 801 Mt Vernon Pl NW, Washington, DC 20001, USA
    • Days of Program: One
    • Timings: 7:30 AM - 5:00 PM
    • Purpose: The market dictated the tracks for this conference, including data access, public/private data partnerships, IoT and more. 
    • Speaker Profile: 
      • Aniel Morgan, Chief Data Officer, USDT 
      • An Neumann, Director, Comcast Applied AI Research, etc.
    • Who were the major sponsors:  
      • Metistream
      • Syntasa
      • Qlik
      • Micro Strategy

    Data Scientist Jobs in Washington, DC

    Logically, the following step sequence needs to be followed for getting a Data Scientist job:

    1. Initial Step: Start by knowing the fundamentals of data science along with the role of a data scientist. Select a programming language, preferably R or Python.
    2. Mathematical understanding: Since data science largely involves making sense of data by finding patterns and relationships between them, you need to have a good grasp of statistics and mathematics, particularly topics like:
      • Descriptive statistics
      • Linear algebra
      • Probability
      • Inferential statistics
    3. Libraries: The process of data science involves tasks like pre-processing data, plotting structured data and application of ML algorithms. The popular libraries include:
      • SciPy
      • Scikit-learn
      • Pandas
      • NumPy
      • Matplotlib
      • ggplot2
    4. Visualizing data: Data scientists need to find patterns in data and make it simple for making sense out of it. Data visualization is popularly done through graphs. The libraries used for that include ggplot2 and matplotlib.
    5. Data pre-processing: Pre-processing of data is done with the help of variable selection and feature engineering to convert the data into a structured form so that it can be analysed by ML tools.
    6. Deep Learning and ML: Along with ML, knowledge of deep learning is preferable since these algorithms help in dealing with huge data sets. You should take time learning topics such as neural networks, RNN and CNN.
    7. NLP: All data scientists are required to have expertise in Natural Language Processing, which involves processing and classification of text data form.
    8. Brushing up skills: You can take your skills to the next level by taking part in competitions such as Kaggle. You can also work on your own projects to polish your skills.

    The steps given below can help you improve your chances of getting data scientist jobs:

    • As a part of interview preparation cover the important topics such as:
      • Statistics
      • Probability
      • Statistical models
      • Understanding of neural networks
      • Machine Learning
    • You can build and expand your network and connections through data science meetups and conferences
    • Participation in online competitions can help you test your own skills
    • Referrals can be helpful for getting data science interviews, so you should keep your LinkedIn profile updated.
    • Finally, once you think you are ready, go for the interview.

    The profession of data scientist involves discovery of patterns and inference of information from huge amounts of data, to meet the goals of a business.

    Nowadays, data is being generated at a rapid rate, which has made the data scientist job even more important. The data can be used for discovering ideas and patterns that can potentially help advance businesses. A data scientist has to extract information out of data and make relevant sense out of it for benefitting the business.

    Roles and responsibilities of data scientists:

    • Fetching relevant data from structured and unstructured data
    • Organizing and analyzing the extracted data
    • Making sense of data through ML techniques, tools and programs
    • Statistically analyzing data and predicting future outcomes

    As compared to other professionals in predictive analytics, data scientists have 36% higher base salary. The pay for the job depends on the following factors:

    • Company type:
      • Governmental & Education sector: Lowest pay
      • Public: Medium pay
      • Start-ups: Highest pay
    • Roles & Responsibilities:
      • Data scientist: $113,436/yr
      • Data analyst: $65,332/yr
      • Database Administrator: $93,064/yr

    A data science career path can be explained through the following roles:

    • Business Intelligence Analyst: This role requires figuring out the trends in the business and the market. It is done through data analysis.
    • Data Mining Engineer: The job of a Data Mining Engineer is to examine data for business as well as a third party. He/she also has to create algorithms for aiding the data analysis.
    • Data Architect: Data Architects work with users and system designers and developers for creating blueprints used by DBMS for integrating, protecting, centralizing and maintaining data sources.
    • Data Scientist: Data Scientists performs analysis of data and develops a hypothesis by understanding data and exploring its patterns. Thereafter, they develop systems and algorithms for productive use of data for the interest of business.
    • Senior Data Scientist: The role of Senior Data Scientists is anticipating future business needs and accordingly shaping the present project, data analyses and systems.

    The top professional associations and groups for Data Scientists in Washington,DC are:

    • Data Science DC
    • Full Stack Data Science
    • Data Education DC
    • Big Data, Analytics, and Artificial Intelligence
    • Women and NB Data Scientists DC

    Apart from referrals, other effective ways of networking with data scientists in Washington,DC include:

    • Online platforms such as LinkedIn
    • Data Science Conferences
    • Meetups and other social gatherings

    There are numerous career options in the field of data science in Washington,DC, including:

    • Data Scientist
    • Data Analyst
    • Data Architect
    • Marketing Analyst
    • Business Analyst
    • Data Administrator
    • Business Intelligence Manager
    • Data/Analytics Manager

    Some key points that employers look for while employing data scientists include:

    •   Qualification and Certification: Having high qualification is a must and certain certifications also help
    •   Python: Python programming is highly used and is usually preferred by companies
    •   Machine Learning: It is an absolute must to possess ML skills
    •   Projects: Working on real world projects not only helps you learn data science but also build your portfolio

    Data Science with Python Washington, D.C.

    Below are some of the reasons why python is considered as the most popular language to learn data science- 

    • Simple and Readable: It is highly preferred by data scientists over other programming languages due to its simplicity and the dedicated packages and libraries made particularly for data science use.
    • Diverse resources: Python gives data scientists access to a broad range of resources, which helps them solve problems that may come up during the development of a Python program or Data Science model.
    • Vast community: The community for Python is one of its biggest advantages. Numerous developers use Python every day. So, a developer can get help from other developers for resolving his/her own problems. The community is highly active and generally helpful .

    The field of data science is huge involving numerous libraries and it is important to choose a relevant programming language.

    • R: It offers various advantages, even though the learning curve of the language is steep.
      • Huge open source community with high quality packages
      • Availability of statistical functions and smooth handling of matrix operations
      • Data visualization tool through ggplot2
    • Python: It is one of the most popular languages in data science, even though it has fewer packages in comparison to R. 
      • Easier learning and implementation
      • Huge open-source community
      • Libraries required for the purpose of data science are provided through Panda, tensorflow and scikit-learn
    • SQL: This structured query language works on relational database
      • The syntax is readable
      • Allows efficient updation, manipulation and querying of data.
    • Java: It doesn’t not have that many libraries for the purpose of data science. Even though its potential is limited, it offers benefits like:
      • Integrating data science projects is easier since the systems are already coded in Java
      • It is a compiled and general-purpose language offering high performance
    • Scala: Running on JVM, Scala has complex syntax, yet it has certain uses in the field of data science.
      • Since it runs on JVM, programs written in Scala are compatible with Java too
      • High performance cluster computer is achieved when Apache Spark is used with Scala.

    Python 3 can be installed on Windows by following the given steps:

    • Visit the download page to download the GUI installer and setup python on Windows. During installation, you need to select the bottom checkbox for adding Python 3.x to PATH. This is your classpath that will allow using of functionalities of python via terminal.
    • Python can also be alternatively installed via Anaconda. The given command can be used to check the version of any existing installation:        python –version
    • The following command can be used for installing and updating two of the most crucial third party libraries:
      python -m pip install -U pip

    Virtualenv can also be used for creation of isolated python environments and python dependency manager called pipeny.

     Python 3 can be installed from its official website via a .dmg package. However, Homebrew is recommended for installation of python and its dependencies. The following steps will aid in the installation of Python 3 on Mac OS X:

    1. Xcode Installation: Apple’s Xcode package is required for installation of brew. So, you need to start by executing the given command:$ xcode-select –install
    2. Brew Installation: Homebrew can be installed with the help of given command:
      /usr/bin/ruby -e "$(curl -fsSL"
      The installation can be confirmed by using: brew doctor
    3. Python 3 installation: Use the given command for installing the latest version of Python:
      brew install python
      Confirm the python version using: python –version

    Installation of virtualenv will allow running different projects

    reviews on our popular courses

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    The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, Knowledgehut!

    Ong Chu Feng

    Data Analyst
    Attended Data Science with Python Certification workshop in January 2020
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    I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, and leading us through a difficult topic. KnowledgeHut is a great place to learn the skills that are coveted in the industry.

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    Network Administrator.
    Attended Agile and Scrum workshop in May 2018
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    Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

    Godart Gomes casseres

    Junior Software Engineer
    Attended Agile and Scrum workshop in May 2018
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    I feel Knowledgehut is one of the best training providers. Our trainer was a very knowledgeable person who cleared all our doubts with the best examples. He was kind and cooperative. The courseware was excellent and covered all concepts. Initially, I just had a basic knowledge of the subject but now I know each and every aspect clearly and got a good job offer as well. Thanks to Knowledgehut.

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    Senior Web Administrator
    Attended Agile and Scrum workshop in May 2018
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    This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.

    Barton Fonseka

    Information Security Analyst.
    Attended PMP® Certification workshop in May 2018
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    The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

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    Network Engineer
    Attended PMP® Certification workshop in May 2018
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    Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut staff was friendly and open to all questions.

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    Senior Network Architect
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    The Course

    Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

     Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

    • Get advanced knowledge of data science and how to use them in real life business
    • Understand the statistics and probability of Data science
    • Get an understanding of data collection, data mining and machine learning
    • Learn tools like Python

    By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

    Tools and Technologies used for this course are

    • Python
    • MS Excel

    There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

    On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

    Your instructors are Python and data science experts who have years of industry experience. 

    Finance Related

    Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

    KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

    The Remote Experience

    In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

    Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

    Have More Questions?

    Data Science with Python Certification Course in Washington, DC

    Named after George Washington, power is the reason why Washington exerts such a palpable hum. Teeming with iconic monuments, huge museums and the corridors of power, Washington is home to all three segments of the federal government that includes the White House, the Supreme Court, and the Capital Building. It also hosts the State Department, Pentagon, the World Bank and embassies from across the globe. It is an amazing experience to visit the White House, to see the Capitol chamber and see senators hold sessions. Known for its museums, Washington?s monuments bear honour to both the beauty of American arts, from the breathtaking Lincoln Memorial to the powerful Vietnam Veterans Memorial to the contentious Martin Luther King Jr. Memorial. KnowledgeHut offers a range of professional courses here including-- PMP, -ACP, PRINCE2, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, and many more. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.