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

Get hands-on Python skills and accelerate your data science career

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Grow your Data Science skills

Data Science is one of the most sought-after technologies used by organizations the world over to fuel creative projects and collect data efficiently from both structured and unstructured sources. So, the demand for Data Science isn't slowing down in the near future. We'll help you catch up with our Data Science with Python course in Washington.

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  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Why get the Data Science with Python certification in Washington


Data scientists across the world stress the importance of learning Python if you want a flourishing career in Data Science. It has a rich collection of libraries, data structures, and tools, which will help you script applications or websites quickly. The Data Science for Python course from KnowledgeHut will help you learn what Data Science really is.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.


Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course in Washington. 
  • Elementary programming knowledge will be of advantage.

Who should attend the Data Science with Python course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules for Washington

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What you will learn in the Data Science with Python course

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


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

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization


  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study


  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value


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


  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.


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


  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study


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


  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

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


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


  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing 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.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


  • Project to be selected by candidates.

FAQs on Data Science with Python Course in Washington

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

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

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at and we will be happy to get back to you.

Additional FAQs on Data Science with Python Training in Washington

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

    What Learners Are Saying

    Ong Chu Feng Data Analyst
    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!

    Attended Data Science with Python Certification workshop in January 2020

    Lauritz Behan Computer Network Architect.

    Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.

    Attended PMP® Certification workshop in May 2020

    Merralee Heiland Software Developer.

    KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and concepts.

    Attended PMP® Certification workshop in April 2020

    Astrid Corduas Telecommunications Specialist

    The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and smoothed out the whole process.

    Attended Agile and Scrum workshop in June 2020

    Raina Moura Network Administrator.

    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.

    Attended Agile and Scrum workshop in January 2020

    Prisca Bock Cloud Consultant

    KnowldgeHut's training session included everything that had been promised. The trainer was very knowledgeable and the practical sessions covered every topic. World class training from a world class institue.

    Attended Certified ScrumMaster (CSM)® workshop in January 2020

    Anabel Bavaro Senior Engineer

    The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.

    Attended Certified ScrumMaster (CSM)® workshop in August 2020

    Alexandr Waldroop Data Architect.

    The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and comprehenisve. Thanks to KnowledgeHut. Looking forward to more such workshops.

    Attended Certified ScrumMaster (CSM)® workshop in January 2020

    Data Science with Python Certification Training in Washington

    About Washington 

    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 honor to the beauty of American arts, from the breathtaking Lincoln Memorial to the powerful Vietnam Veterans Memorial to the contentious Martin Luther King Jr. Memorial.  

    Data Science with Python Training in Washington 

    KnowledgeHut offers a range of professional courses. Along with Data Science Course with Python, we also have PMP, PMI-ACP, PRINCE2, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache, Hadoop, SAFe Practitioner, and many more.  

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