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 analysing, 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 an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
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Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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:
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:
Some behavioural traits a data science professional should have include:
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:
The top business skills required for becoming a data scientist include:
The following ways can help you brush up your skills in data science:
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:
Given below are the steps needed to become top data scientist:
To prepare for a data science career, you need to follow the given steps and incorporate the appropriate skills:
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:
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:
Programming knowledge is a must for any aspiring data scientist because:
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.
|1.||2019 Dataworks Summit||20-23 May, 2019||Marriott Marquis Washington, DC, Massachusetts Avenue Northwest, Washington, DC, USA|
|2.||AI World | Government Conference and Expo||24-26 June, 2019|
Ronald Reagan Building and International Trade Center 1300 Pennsylvania Ave NW Washington, DC 20004
|3.||Data-Driven Government||25 September, 2019||Capital Hilton 16th & K Street, NW Washington, DC 20036|
|4.||Chief [Data] Analytics Officers & Influencers||29-30, May 2019||The Embassy Suites by Hilton Washington DC Convention Center, 900 10th Street NW, Washington, District of Columbia, 20001, USA|
|5.||Subsurface Data and Machine Learning||June 6, 2019|
National Academy of Sciences 2101 Constitution Ave NW Room 125 Washington, DC 20001, United States
1. 2019 Dataworks Summit, Washington
2. AI World | Government Conference and Expo, Washington
3. Data-Driven Government, Washington
4. Chief [Data] Analytics Officers & Influencers, Washington
5. Subsurface Data and Machine Learning, Washington
|1.||The Washington Big Data Conference 2017||02/10/2017||Walter E. Washington Convention Center, 801 Mt Vernon Pl NW, Washington, DC 20001, USA|
1. The Washington Big Data Conference 2017
Logically, the following step sequence needs to be followed for getting a Data Scientist job:
The steps given below can help you improve your chances of getting data scientist jobs:
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:
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:
A data science career path can be explained through the following roles:
Apart from referrals, other effective ways of networking with data scientists in Washington,DC include:
There are numerous career options in the field of data science in Washington,DC, including:
Some key points that employers look for while employing data scientists include:
Below are some of the reasons why python is considered as the most popular language to learn data science-
The field of data science is huge involving numerous libraries and it is important to choose a relevant programming language.
Python 3 can be installed on Windows by following the given steps:
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:
Installation of virtualenv will allow running different projects
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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. Payscale.com 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.
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
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
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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
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