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.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
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).
Tons of data is generated every day and data scientists are required to analyze this data. More and more companies are attempting to drive value and revenue from their data. For three years in a row, data scientist has been named the number one job in the U.S by Glassdoor. In Chicago, Bank of America, McKinsey & Company, Shirley Ryan Ability Lab, Grubhub, Wolverine Trading, Deloitte, McDonald’s Corporate, Amazon Web Services, Strata Decision Technology, etc. are some of the companies that are looking for expert data scientists to help them make data-driven decisions.
Data science is a vast field. The University of Chicago, University of Illinois, Depaul University, Illinois Institute of Technology, and Loyola University offer postgraduate degrees in Data Science that will help you get all the technical skills required to become a data scientist. To become an expert in this field, you need to have the following technical skills:
Below are the top essential behavioral traits of a successful Data Science professional:
Data Science is in demand. So naturally, data scientists enjoy certain benefits over other IT jobs and these include:
Below are the must-have Business Skills needed to become a Data Scientist:
Data Scientists are in huge demand in Chicago, Illinois. Several companies are willing to pay generously to data scientists including Crowe, Relativity, TransUnion, Technomic, USG, Mattersight, KAR Auction Services, Allstate, Optiver US, Ogilvy, Johnson Controls, Groupon, CNH Industrial, etc.
If you are looking for a job as a Data Scientist, you need to brush up your Data Science skills. Here are the 5 best ways to do it:
Data Scientists are in huge demand right now. Organizations ranging from small and mid-sized to large corporations are looking for data scientists to join their team. In Chicago, companies like Amazon Web Services, Bank of America, Strata Decision Technology, Groupon, Wolverine Trading, CNH Industrial, Allstate, Johnson Controls, Ogilvy, McDonald’s Corporate, Crowe, Relativity, Deloitte, USG, Technomic, Optiver US, Mattersight, etc. are looking for skilled data scientists.
The more you practice, the more your data science skills will improve. Here are a few datasets that you can easily find online and practice your data science skills on. They have been categorized according to your expertise level:
If you want to become a top-notch data scientist, you need to follow these steps:
If you want to become a data scientist, here is what you need to do:
A degree from a prestigious institution can help you get a head start in your career. In Chicago, several institutions offer Master's in Data Science like the University of Chicago, University of Illinois, Depaul University, Illinois Institute of Technology, and Loyola University. Here are some advantages of getting a degree in Data Science:
This scorecard will help you decide if you need to have a Master’s degree or not. If your score is more than 6 points, then a Master’s degree is advised:
Yes, programming knowledge is a must to become a data scientist. Here are the reasons explaining why:
A Data Scientist can earn an average of about $110,925 per year in Chicago.
In Chicago, the average annual salary of a Data Scientist is $110,925. On the other hand, in Boston, the average annual salary is $125,310.
In Chicago, the average salary of a data scientist is $110,925 as compared to $122,328 in Washington.
A data scientist earns an average of about $110,925 every year in Chicago as compared to $128,623 in New York.
The average annual salary of a data scientist in Chicago is $110,925 whereas the average salary is $113,568 in Rockford.
In Chicago, a data scientist earns an average of about $110,925 per year. In Peoria, on the other hand, this salary is about $83,956 every year.
The average annual salary of a data scientist in Chicago is $110,925 as opposed to $58,577 in Springfield.
Data Scientist is the hottest tech job in Chicago right now. Mainstream companies have started to harness the power of Big Data and this has skyrocketed the job opportunities for Data Scientists in the city.
The benefit of being a Data Scientist in Chicago is that there are many companies in the city that have started to invest in building a data scientist team. So, you will have an opportunity to be a part of something big from the very beginning.
Chicago is a great place for your Data Science career. Chicago is considered a “small big city” which means that you will have a data science conference every other night and the robust community will help you become a known data scientist in the city. The main perk of being a data scientist is that they are not bound to a particular field. With all the major companies of all fields investing in the Data Science, Data Scientists have a wide array of fields to choose from. Also, the cost of living is slightly lower than the big cities like Manhattan.
Some of the top companies hiring Data Scientists in Chicago are Civic Analytics, Enova, Avant, KAR, Uptake, and Aginity.
|1.||Data Science Conference, Chicago||14-15 May, 2019||University of Chicago: Gleacher Center, Chicago, USA|
|2.||Chief Data & Analytics Officer Exchange||August 4-6, 2019||The Drake Hotel, Chicago, IL|
|3.||Midwest Applied AI Conference, Chicago||May 20, 2019||The AON Center|
|4.||Insurance AI and Analytics USA, Chicago||May 2-3, 2019||Renaissance Chicago Downtown|
|5.||Data Science Immersive - Advance your career with Python||May 20th - May 24th||Practical Programming Chicago, 29 E Madison Street, 16th Fl. 1620, Chicago, IL 60602, United States|
|6.||Chicago AI & Data Science Conference 2019, Chicago||May, 2019||University of Chicago, Gleacher Center, Room 621|
|7.||Data Connectors Chicago Cybersecurity Conference 2019, Chicago||May 9, 2019||Intercontinental Chicago Magnificent Mile|
|8.||TDWI Chicago Conference, Chicago||April 28–May 3||TDWI, Hilton Chicago|
|9.||The Business of Data Science – Chicago||April 30-May 1, 2019||Summit Chicago|
1. Data Science Conference, Chicago
2. Chief Data & Analytics Officer Exchange, Chicago
3. Midwest Applied AI Conference, Chicago
4. Insurance AI and Analytics USA, Chicago
5. Data Science Immersive - Advance your career with Python, Chicago
6. Chicago AI & Data Science Conference 2019, Chicago
7. Data Connectors Chicago Cybersecurity Conference 2019, Chicago
8. TDWI Chicago Conference, Chicago
9. The Business of Data Science – Chicago
|1.||Insurance AI and Analytics USA||June 27-28, 2018||Renaissance Chicago Downtown Hotel, 1 West Upper Wacker Drive, Chicago|
|2.||Women In Data Science 2018||Monday, March 5, 2018||626 W. Jackson Rd. · Chicago, IL|
|3.||Chicago AI & Data Science Conference 2018||Sat, May 19, 2018||The University of Chicago, Gleacher Center, Room 621|
|4.||Predictive Analytics Innovation Summit||October 30 - 31, 2018||Sheraton Grand Chicago, 301 E North Water St, Chicago, IL 60611, USA|
1. Insurance AI and Analytics USA, Chicago
2. Women In Data Science 2018, Chicago
3. Chicago AI & Data Science Conference 2018, Chicago
4. Predictive Analytics Innovation Summit, Chicago
If you want to get a job as a Data Scientist, you need to follow the below-mentioned learning path:
Here are the 5 important steps to help you prepare for the job as a data scientist:
As a data scientist, you are responsible for the following:
Data science is a vast field with many different tools. Here is the data science career path explained in detail:
Business Intelligence Analyst: The role of a business intelligence analyst is to convert data into useful information that can be used to make sound business decisions.
Data Mining Engineer: A data mining engineer is responsible for deriving and improving the quality of the data using spatial and temporal analysis. He/she will be working on the creation of statistical and predictive models and algorithms to analyse very large data sets.
Data Architect: A data architect works alongside developers, system designers, and users to create blueprints that are used for integrating, centralizing, maintaining, and protecting the data sources.
Data Scientist: The responsibility of a Data Scientist is to derive value out of data. He/she should also be able to perform statistical analysis.
Senior Data Scientist: A senior data scientist’s job is to successfully shape the projects and systematize it in a way that meets the needs of the business.
The top 8 data science career opportunities in 2019 are –
Most companies generally prefer data scientists to have mastery over some software and tools. They usually look for:
Python is the most sought after programming language used in data science. Here is why:
The 5 most popular programming language commonly used in Data Science:
To download and install Python 3 on Windows, you need to follow these steps:
python -m pip install -U pip
For downloading and installing Python 3 on Mac OS X:
$ Xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" For confirming the installation, type: brew doctor
brew install python
Confirm the python’s version using the command: python --version
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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.
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.
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