Data Science with Python Training in Chicago, IL, United States

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

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
  • 70 + Countries and counting

Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 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 Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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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.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

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

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be useful.

Who should attend this course?

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 researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

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

2

User-defined functions in Python

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

3

Datasets and manipulation

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

4

Probability and Statistics

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

5

Advanced Statistics

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

6

Predictive Modelling

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

7

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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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.


Topics

  • 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

Topics

  • 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

Hands-on

  • 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

Topics

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

Hands-on

  • 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.

Topics

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

Hands-on

  • 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 & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • 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.

Topics

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

Hands-on

  • 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.


Hands-on

  • Project to be selected by candidates.

Frequently Asked Questions

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 support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

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 un View More

Attended Data Science with Python Certification workshop in January 2020

Vito Dapice

Vito Dapice

Data Quality Manager

5/5

The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good View More

Attended PMP® Certification workshop in April 2020

Mirelle Takata

Mirelle Takata

Network Systems Administrator

5/5

My special thanks to the trainer for his dedication and patience. I learned many things from him. I would also thank the support team for their help. It was well-organised, great work Knowledgehut team!

Attended Certified ScrumMaster (CSM)® workshop in July 2020

Yancey Rosenkrantz

Yancey Rosenkrantz

Senior Network System Administrator

5/5

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, View More

Attended Agile and Scrum workshop in April 2020

Meg Gomes casseres

Meg Gomes casseres

Database Administrator.

5/5

The Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. I would certainly recommend it to my team.

Attended PMP® Certification workshop in January 2020

Barton Fonseka

Barton Fonseka

Information Security Analyst.

5/5

This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his View More

Attended PMP® Certification workshop in July 2020

Marta Fitts

Marta Fitts

Network Engineer

5/5

The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and View More

Attended PMP® Certification workshop in May 2020

Kayne Stewart slavsky

Kayne Stewart slavsky

Project Manager

5/5

The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.

Attended PMP® Certification workshop in June 2020

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Data Science with Python

What is Data Science

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:

  1. Python Coding: Programming is a must-have skill to become a data scientist. Python is the most preferred language used in the field of data science. It helps in the preprocessing of the data. It is simple, versatile, and can take various formats of data. This makes it one of the most important technical skills required to become a data scientist.
  2. R Programming: Knowledge of R programming will help you solve data science problems easily. It is widely used by data miners for developing statistical software and data analysis.
  3. Hadoop Platform: Hadoop is a platform used to facilitate data science processes. Although not a must, knowledge of Hadoop is highly recommended as it is used in several data science projects.
  4. SQL database and coding: A data scientist has to deal with databases and  to access and communicate with the database; SQL or Structured Query Language is required. You need to know the formation and structure of the database.
  5. Machine Learning and Artificial Intelligence: Having machine learning and artificial intelligence skills is a must to become a data scientist. Make sure that you are familiar with topics like reinforcement learning, neural network, adversarial learning, decision trees, machine learning algorithms, etc.
  6. Apache Spark: It is a data-sharing technology that is used for big computation. It is faster than Hadoop because it caches its computation in the system’s memory. Hadoop, on the other hand, reads and writes to the disk. Spark is also capable of handling large datasets and complex unstructured data.
  7. Data Visualization: After the data analysis has been done, a data scientist has to visualize this data using graphs and charts to help the non-technical members of the team understand it better. There are tools like ggplot, matplotlib, Tableau, and d3.js that are used for data visualization. All these tools convert the complex results obtained from the analysis into a format that can be easily understood. Visualization also helps the data scientists in grasping the insights from data and using it to make crucial marketing decisions.
  8. Unstructured data: Tons of data is generated every day. Most of this data is unorganized and unlabelled. This unstructured data cannot be analyzed. Examples of such data include social media posts, customer reviews, blog posts, audios, and videos.

 

Below are the top essential behavioral traits of a successful Data Science professional:

  • Curiosity – A data scientist must be curious and thirsty for knowledge. With so much data comes so many opportunities to gain insights. To help mine this data, one requires curiosity.
  • Clarity – Data science is a vast field that can get confusing at times. It is important for a data scientist to have clarity at every step, including while cleaning up the data or writing the code.
  • Creativity – Creativity is required in the field of data science to figure out what is missing from the data, deciphering the patterns, and finding out ways to get the desired results. It is the job of a data scientist to design new tools for analysis, new features and new ways of visualizing the data.
  • Skepticism – Although creativity is a required behavioral trait, skepticism is as much important. A data scientist must stay in the real world and not get carried away by his/her creativity

Data Science is in demand. So naturally, data scientists enjoy certain benefits over other IT jobs and these include:

  1. High Pay: There is a gap between demand and supply of data scientists. This allows data scientists to fetch higher salaries. The average salary for a Data Scientist in Chicago is $109,547.
  2. Good bonuses: If a company hires you as a data scientist, you can expect perks like signing bonus, equity shares, and year-end bonuses. The average additional cash compensation for a Data Scientist in Chicago is $7,073
  3. Education: As a data scientist, you will have either a Master's degree or a Ph.D. This allows you to be eligible for working as a researcher or a lecturer in any institute.
  4. Mobility: Most of the companies that hire data scientists are based in developed countries. So, getting a job in one will get you a handsome salary and an improved living standard.
  5. Network: As a data scientist, you will get the opportunity to visit tech talks, meetups, and conferences and socialize with other fellow data science professionals. You need to build your network and expand your professional connections for referral purposes.

Data Scientist Skills and Qualifications

Below are the must-have Business Skills needed to become a Data Scientist:

  1. Analytic Problem-Solving – Before you can strategize a solution for a problem, you need to have an understanding of the problem. 
  2. Communication Skills – A data scientist needs to communicate with the management and the users about the deep business and customer analytics. This requires good communication skills.
  3. Intellectual Curiosity – Curiosity is a must for a data scientist. You need to ask questions and have a thirst for producing results and providing value to the business.
  4. Industry Knowledge – To analyze the data, a data scientist must be aware of the industry they are working in. This helps them in knowing what is relevant and what is not.

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:

  • Boot camps: Bootcamps are a great way to move into a new career with little experience. They help you get the required theoretical knowledge and practical experience in that short duration of time.
  • MOOC courses: These online courses contain assignments that will help you brush up your implementation skills. Taught by data science experts, these courses will help you gain knowledge of new trends in the industry.
  • Certifications: Certifications will not only help you brush up your data science skills but also improve your CV. Here are a few data science certifications:
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Projects: The best way to practice your skills is through projects. You can either start by creating your projects or finding alternate solutions to previous problems.
  • Competitions: Online competitions like Kaggle will help you improve your data science and problem-solving skills. In these competitions, you have to offer a solution to a problem while following all the restraints and satisfying all the requirements.

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:

  • Beginner Level
    • Iris Data Set: This classification problem with 4 columns and 50 rows, is the most easy, resourceful, and versatile dataset available. It is used for pattern recognition.Practice Problem: Determining the class of the flower using the parameters.
    • Bigmart Sales Data Set: This retail dataset is a regression problem which has 12 variables and 85,223 rows. You will be able to understand operations like inventory management, customizations, and product bundling, etc., these operations require data science and business analytics.Practice Problem: Determining the store’s total sales.
  • Intermediate Level:
    • Black Friday Data Set: This dataset contains transactional data of millions of customers. It has 12 columns and 550,069 rows.Practice Problem: Predicting the total purchase amount.
    • Human Activity Recognition Data Set: This dataset is collected using the recordings of smartphones collected using inertial sensors. It is a collection of 30 human subjects. The dataset consists of 561 columns and 10,299 rows.Practice Problem: Predicting human activity’s category.
  • Advanced Level:
    • Identify the digits data set: With 7000 images of 82X28 dimensions each, this dataset studies, analyzes and recognizes every single element present in the image.Practice Problem: Identifying the elements present in the image.
    • Vox Celebrity Data Set: Used for audio processing in deep learning, this data set isolates speech. It contains 100,000 words spoken by 1,251 celebrities extracted through YouTube videos.Practice Problem: Identifying the celebrity’s voice.

How to Become a Data Scientist in Chicago, Illinois

If you want to become a top-notch data scientist, you need to follow these steps:

  • Get started: Select a programming language that you are comfortable with. Both Python and R are amongst the most popular languages for data analysis.
  • Mathematics and statistics: You need to have mathematics and statistics skills for analyzing data, deciphering patterns, and figuring out the relationships present in the data.
  • Data visualization: It is an important step as it makes the analysis understandable for the user and the non-technical members of the team.
  • ML and Deep learning: You will need these skills to create the tools that analyze the data.

If you want to become a data scientist, here is what you need to do:

  1. Degree/certificate: First step is to get a degree or a certificate. It will help you get started in the field of data science. You can try online or offline programs, depending on whatever suits your needs. Your course will cover the basics of the field and the latest market trends.
  2. Unstructured data: The responsibility of a data scientist is to find patterns in the data after analyzing it. But the data that is collected is mostly in an unstructured form, meaning it is unlabelled and unorganized. You need to prepare it for analysis.
  3. Software and Frameworks: Software and frameworks help to facilitate the data science processes smoothly. You need to have an understanding of a programming language like Python and R. Apart from this; you need to have hands-on experience in working with Hadoop or Apache Spark. Lastly, you must be familiar with SQL and databases. This includes writing and reading SQL queries.
  4. Machine learning and Deep Learning: Once the data is collected and cleaned, algorithms are applied to this data to analyze it. You will need to use your deep learning skills for training the model.
  5. Data visualization: After the analysis is done, it is the responsibility of a data scientist to convert the complex result into a format that can be easily comprehended. You need to use graphs and charts to analyze the data. There are tools like matplotlib, ggplot2, etc. that are used for data visualization.

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:

  • Networking – This is the place where you start building your network. This networking will benefit you a lot in future as this industry works on referrals. 
  • Structured learning – If you are not good at self-learning, a degree might be the best way for you to become a data scientist. You will have to keep up with the curriculum and follow a schedule.
  • Internships – The internship will help you get an opportunity to gain practical, hands-on experience in the field of data science.
  • Recognized academic qualifications for your résumé – A degree will surely improve your CV and jumpstart your career.

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:

  • Strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • Weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • Non-STEM background: 5 points
  • < 1 year of experience in Python: 3 points
  • 0 year of experience in regular coding for a job: 3 points
  • Not good at independent learning: 4 points
  • Don’t understand that this scorecard is a regression algorithm: 1 point

Yes, programming knowledge is a must to become a data scientist. Here are the reasons explaining why:

  • Data sets: While working on data science projects, you will have to deal with several, huge datasets. You will need the knowledge of a programming language to analyze these datasets.
  • Statistics: You need statistical skills to analyze the data. But you need programming skills to implement your statistical knowledge.
  • Framework: Programming language is required to create frameworks that can be used to automatically analyze experiments, visualize the data, and manage thea data pipeline.

Data Scientist Salary in Chicago, Illinois

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. 

Data Science Conference in Chicago, Illinois

S.NoConference nameDateVenue
1.Data Science Conference, Chicago14-15 May, 2019University of Chicago: Gleacher Center, Chicago, USA
2.Chief Data & Analytics Officer ExchangeAugust 4-6, 2019The Drake Hotel, Chicago, IL
3.Midwest Applied AI Conference, ChicagoMay 20, 2019The 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

  • About the Chicago conference: It provides a vendor-free, sponsor-free, and recruiter-free meeting space for data scientists.
  • Event Date: 14-15 May, 2019
  • Venue: University of Chicago: Gleacher Center, Chicago, USA
  • Days of Program: 2
  • Timings: 09:00 AM-06:00 PM
  • Purpose: Advisory board, speakers, and attendees come from all fields of business analytics and academia like data science, big data, data mining, machine learning, artificial intelligence, or predictive modeling.
  • How many speakers: 19
  • Speakers & Profile: 
    • Misha Ghosh, Head of Product Development & Innovation, Retail Commercial Real Estate Information Services at UNC Charlotte, Charlotte, USA
    • Nurur Rahman, Ph.D., Data Scientist at TIAA-CREF, USA
    • Gene Leynes, Data Scientist at City of Chicago, USA
    • Patrick(Yu) Zhao, Business Intelligence Data Scientist at Amazon USA
    • Jim Newswanger, IBM CHQ Social Analytics | Insights at IBM USA
    • Ling Zhang, Sr. Manager of Advanced Analytics - Data Scientist at Comcast USA
    • Bamshad Mobasher, Professor of Computer Science at Center for Web Intelligence USA
    • Nick Kadochnikov, Data Scientist, Educator and Business Analytics Consultant: IBM and University of Chicago at the University of Chicago Graham School USA
    • Dana Ferguson, Quantitative Research & Analytics at Allstate USA
    • Shavar Beckford, Data Scientist at Allstate USA
    • George Roumeliotis, Distinguished Data Scientist at Walmart USA
    • Pratik Agrawal, Senior Consultant (Data Science/Big Data), IRI at The University of Chicago USA
    • Zachary Martin, AVP Advanced Analytics, Chief Data Scientist at TD USA
    • Scott Burger, Principal Data Scientist at Microsoft USA
    • Dong Li
    • Tanay Chowdhury, Data Scientist at Zurich North America USA
    • Brandon Rohrer
    •  Mike Jaron, Associate Data Scientist at Google USA
    • Nalini Polavarapu, Global Analytics Lead at Monsanto Company USA

2. Chief Data & Analytics Officer Exchange, Chicago

  • About the Chicago conference: The conference will bring together the professionals across industries for in-depth discussions and exchanges of experience and ideas.
  • Event Date: August 4-6, 2019
  • Venue: The Drake Hotel, Chicago, IL
  • Days of Program: 3
  • Purpose: The purpose is to address the emerging landscape of digital business in a digital economy. 
  • How many speakers: 9
  • Speakers & Profile:
    • Tim Eisenmann, Chief Analytics Officer, American Tire Distributors
    • Raj Subbu, Director, Data Science & Analytics, Pratt & Whitney
    • Somesh Nigam, SVP, Chief Analytics and Data Officer, Blue Cross and Blue Shield of Louisiana
    • Michael Roberts, Director of Customer Engagement, ASPE
    • Thomas Dunlap, Chief Data Officer, Information Services Division, London Stock Exchange Group (LSEG)
    • Andrew Reiskind, Senior Vice President, Data Strategy, MasterCard
    • Ash Dhupar, Chief Analytics Officer, Publishers Clearing House
    • Joe Caserta, President and Founder, Caserta
    • Cary Correia, Chief Commercial Data Scientist, General Electric
  • Who are the major sponsors:

    • Aginity
    • AllSight 
    • ASPE Training
    • BlueData
    • Board International
    • Caserta Concepts
    • Civis Analytics
    • Collibra
    • Datometry
    • Duo Security
    • Factual
    • Hypernet
    • IBM Watson
    • Kavi Global
    • NEOS
    • OmniSci
    • Oracle Corporation
    • Qlik®
    • Quantexa
    • SAS
    • Stibo Systems
    • Tamr
    • Tealium 
    • ThoughtSpot
    • Tiger Analytics
    • Vertica
    • WebbMason Analytics
    • Thoughtexchange 

3. Midwest Applied AI Conference, Chicago

  • About the Chicago conference: Top AI leaders will explain how they apply and operationalize AI. 
  • Event Date: May 20, 2019
  • Venue: The AON Center
  • Days of Program: 1
  • Timings: 8:00 AM to 5:00 PM
  • Purpose: Learn how enterprises productize AI offerings and bring them to market. Learn AI case studies in Healthcare, Finance, Consumer Products, Industrial IOT and other applications. 
  • How many speakers: 30

 4. Insurance AI and Analytics USA, Chicago 

  • About the Chicago conference: The conference is bringing together over 450 senior analytics executives and business leaders to discuss game-changing strategies for organization-wide implementation that delivers actual ROI in product, underwriting, customer service, product innovation and claims.
  • Event Date: May 2 -3, 2019
  • Venue: Renaissance Chicago Downtown
  • Days of Program: 2
  • Purpose: Understand the latest technologies that are transforming the industry across all business lines, meet and form new partnerships and hear from the world’s foremost technology experts and innovators in insurance.
  • How many speakers: 40+
  • Who are the major sponsors:
    • dotData
    • DataRobot
    • Deloitte

5. Data Science Immersive - Advance your career with Python, Chicago

6. Chicago AI & Data Science Conference 2019, Chicago

  • About the Chicago conference: The theme for this conference is “Technology. Innovation. Career.” The goal is to create a cross-campus data science learning platform and bring together business and academic leaders, young professionals, entrepreneurs and students. 
  • Event Date: May, 2019
  • Venue: University of Chicago, Gleacher Center, Room 621
  • Purpose: Gain insight into the future of data science development as well as the trending technologies.

7. Data Connectors Chicago Cybersecurity Conference 2019, Chicago

  • About the Chicago conference: This conference will cover how to protect your organization. 
  • Event Date: May 9, 2019
  • Venue: Intercontinental Chicago Magnificent Mile
  • Days of Program: 1
  • Timings: 8:00 AM – 5:00 PM

8. TDWI Chicago Conference, Chicago

  • About the Chicago conference: The conference will bring together leading data scientists to explore latest trends. 
  • Event Date: April 28–May 3
  • Venue: TDWI, Hilton Chicago
  • Days of Program: 7
  • Purpose: Get the most applicable training for top data challenges from the industry’s most respected leaders and practitioners. 
  • Registration cost: $2,235 - $4,610

9. The Business of Data Science – Chicago

  • About the Chicago conference: The Chicago conference informs how to harness the power of data science and artificial intelligence for your organization. 
  • Event Date: April 30-May 1, 2019
  • Venue: Summit Chicago
  • Days of Program: 2
  • Timings: 8:30 am to 4:30 pm
  • Purpose: This conference teaches business leaders the fundamentals of data science, how to use it to make better business decisions and how to implement it in the organization.
  • Registration cost: $2,190
S.NoConference nameDateVenue
1.Insurance AI and Analytics USAJune 27-28, 2018Renaissance Chicago Downtown Hotel, 1 West Upper Wacker Drive, Chicago
2.Women In Data Science 2018Monday, March 5, 2018626 W. Jackson Rd. · Chicago, IL
3.Chicago AI & Data Science Conference 2018Sat, 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

  • Conference City: Chicago, USA
  • About: The AI and Analytics USA conference unleashed Analytics, AI & Data training to deliver customer satisfaction and create automation value. 
  • Event Date: June 27-28, 2018
  • Venue: Renaissance Chicago Downtown Hotel, 1 West Upper Wacker Drive, Chicago
  • Days of Program: Two 
  • Timings: 4:00 PM onwards 
  • Purpose: The Insurance AI & Analytics USA Summit summoned important insights and guidelines to professionals who wished to improve their skills
  • Speaker Profile :
    • Will Dubyak - Innovation and Analytics Expert, VP Analytics for Product Development and Innovation, USAA 
    • Glenn Fung - Senior Researcher and Chief Research Scientist, AI & Machine Learning Research Director, American Family Insurance, etc. 
  • Registration cost: $2499 (may vary)
  • Who are the major sponsors:  
    • Deloitte
    • Dot-data
    • OPERA
    • LexisNexis
    • Google Cloud 

    2. Women In Data Science 2018, Chicago

    • Conference City: Chicago, USA
    • About: The global Women in Data Science (WiDS) Conference provided extraordinary support to women in the field and inspired data scientists to participate in the development of data and its auxiliary values. 
    • Event Date: Monday, March 5, 2018
    • Venue: 626 W. Jackson Rd. Chicago, IL
    • Days of Program: One
    • Timings: 5:00 PM - 7:30 PM 
    • Speaker Profile:
      • Ziya Ma, Vice President of Software and Services Group and Director of Big Data Technologies, Intel Corporation 
      • Margot Gerritsen: Bhavani Thurasingham, Professor of Computer Science
    • Registration cost: Free Entry 

    3. Chicago AI & Data Science Conference 2018, Chicago

    • Conference City: Chicago, USA
    • About: The conference focused on AI advancement, data solutions, and their merger. 
    • Event Date: Sat, May 19, 2018
    • Venue: The University of Chicago, Gleacher Center, Room 621
    • Days of Program: One
    • Timings: 9:00 AM – 5:00 PM CDT
    • Purpose: The conference aimed to provide a diverse presentation of Deep Learning, Blockchain, trending topics, including Artificial Intelligence. It brought together both start-ups and established organizations. 
    • Speaker Profile:
      • Erik Widman, AI Product owner, Solstice 
      • Guru Rao, Chief Data and Analytics Officer, Fballiance Insurance 
      • Michael Reddy, President, Authority Partners
    • Registration cost: $25 - $100

    4. Predictive Analytics Innovation Summit, Chicago

    • Conference City: Chicago, USA
    • About: The conference focused on ever-evolving data landscape. The two days were filled with workshops, keynote presentations, new research, and new technologies that aided in learning cutting-edge and advanced technologies.
    • Event Date: October 30 - 31, 2018 
    • Venue: Sheraton Grand Chicago, 301 E North Water St, Chicago, IL 60611, USA
    • Days of Program: Two 
    • Timings: 8:00 AM - 5:10 PM
    • Purpose: The purpose of the conference was to discover new technologies and enhance the advanced strategy, learned best practices of deep learning and AI that help in achieving ROI. 
    • Speaker Profile:
      • Sheela Siddappa, Global Head - Data Science, BOSCH
      • Yi-Chen Tu, Senior Director, Data Science, Capital One
      • Tony Liu, Director of Data Science, AMA
    • Registration cost:  Gold Pass - $1995, Silver Pass - $1695, One Day Pass - $995
    • Who are the major sponsors: 
      • Crowd Reviews
      • QueBit
      • Visibility
      • Women Who Code  

    Data Scientist Jobs in Chicago, Illinois

    If you want to get a job as a Data Scientist, you need to follow the below-mentioned learning path:

    1. Getting started: The first step is to select the programming language that you will be working in. Python and R are the most popular programming languages in the field of data science. Also, read through all the roles and responsibilities of a data scientist.
    2. Mathematics: Next, you need to get yourself familiar with mathematics skills required for collecting data, deciphering patterns, and visualizing this data into an understandable format. You must pay attention to topics like descriptive statistics, inferential statistics, probability, and linear algebra.
    3. Libraries: Libraries and packages like Pandas, SciPy, NumPy, Scikit-learn, matplotlib, ggplot2, etc. are used to carry out processes like data preprocessing, plotting the data, and applying ML algorithms to this data.
    4. Data visualization: Data visualization is an important step required to become a data scientist. This not only makes it more understandable but also allows the data scientist to find patterns. Matplotlib and ggplot2 are used for data visualization.
    5. Data preprocessing: Most of the data that is generated is unlabelled and unorganized. This data cannot be analyzed until it is preprocessed using feature engineering and variable selection. 
    6. ML and Deep learning: Being an expert in machine learning and deep learning is very important to become a data scientist. You need deep learning algorithms to help train the model and a thorough understanding of topics like CNN, RNN, and neural networks.
    7. Natural Language processing: To analyze the textual data, natural language processing is required.
    8. Polishing skills: You can participate in competitions like Kaggle to polish and explore your data science skills. 

    Here are the 5 important steps to help you prepare for the job as a data scientist:

    1. Study: Study as much as you can. Apart from data science, you also must be familiar with the concepts of machine learning, neural networks, probability, statistics, and statistical models.
    2. Meetups and conferences: You need to visit data science tech talks, meetups, and conferences to connect with other data science professionals and build your network.
    3. Competitions: Online competitions like kaggle will help you brush up your data science and problem-solving skills. 
    4. Referral: Referrals have become the primary source of the interview. You need to update and maintain your LinkedIn profile.
    5. Interview: Go for the interview. You might not get the first job that you applied for. But don’t worry. Learn from your mistakes.

    As a data scientist, you are responsible for the following:

    • The first step is to gather the data required. Most of this data will be in an unstructured form.
    • Extract the relevant data and preprocess it to make it ready for analysis.
    • Once the analysis is done, you need to create machine learning techniques, tools and programs to identify patterns in the data and make sense out of it. 
    • The last step is to gather insights and predict future outcomes by performing statistical analysis.

    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.

    Here are the top professional groups and associations for data scientists in Chicago-

    • Data Science Applications Community - Chicago
    • Data Science Dojo - Chicago
    • Chicago Data Science
    • Analytics and Data Science
    • Chicago women in Big Data

    Here is how you can network with other data scientists:

    • Social gatherings like Meetup 
    • Data science conference
    • An online platform like LinkedIn

    The top 8 data science career opportunities in 2019 are – 

    1. Business Analyst
    2. Business Intelligence Manager
    3. Data Administrator
    4. Data Analyst
    5. Data/Analytics Manager
    6. Data Architect
    7. Data Scientist
    8. Marketing Analyst

    Most companies generally prefer data scientists to have mastery over some software and tools. They usually look for:

    • Education: The first step is to get a degree in data science. This will also help improve your CV. You can also try getting some certifications.
    • Programming: Programming skills are a must for a data scientist. You need to begin with the basics and then move on to data science libraries.
    • Machine Learning: Machine learning and deep learning skills are used to create the tools and frameworks needed to analyze the data.
    • Projects: Working on projects will help you get the required data science skills. Do some hands-on, real-world projects to improve your skills and build your portfolio.

    Data Science with Python Chicago, Illinois

    Python is the most sought after programming language used in data science. Here is why:

    • This multi-paradigm programming language is a structured, object-oriented language that comes along with different libraries and packages. 
    • It has a very easy syntax that is readable and understandable. 
    • There are several resources available that can come in handy whenever a data scientist is stuck with a problem.
    • It has the support of a big, global, open-source community. This will help you begin your journey towards working with the larger open source ecosystem.

    The 5 most popular programming language commonly used in Data Science:

    • R: R is a difficult language. However, it has several open-source packages that can help you with data analysis. It can handle complex matrix operations smoothly through statistical functions.
    • Python: Python is the most preferred language used in data science projects. It has a syntax that resembles the English language. Libraries like Pandas, scikit-learn, and Tensorflow can be used for analyzing the data.
    • SQL: SQL has an easy syntax that is used for querying, updating, and manipulating relational databases.
    • Java: Java is used in many data science projects even though it has limited libraries and verbosity. This is because there are already so many systems coded in Java that makes integrating data science projects easier.
    • Scala: This language is a popular language used in the field of data science. It is compatible with Java as it runs on JVM. It allows high-performance cluster computing when used with Apache Spark.

    To download and install Python 3 on Windows, you need to follow these steps:

    • Download and setup: Visit the download page and install python using the GUI installer. To use the python’s functionalities from the terminal, you need to check the box asking for adding Python 3.x to PATH.

    • For checking the python’s version installed in the system, use the command – python –version.
    • Update and install setuptools and pip: Use the following command for installing and updating libraries:

    python -m pip install -U pip

    For downloading and installing Python 3 on Mac OS X:

    • Install Xcode: The first step is to install the Xcode package of Apple using the following command: 

    $ Xcode-select --install

    • Install brew: Next, install the Apple’s package manager, Homebrew by typing in the following command: 

    /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" For confirming the installation, type: brew doctor

    • Install Python 3: To install python, use the following command:

    brew install python

    Confirm the python’s version using the command: python --version

    Data Science with Python Certification Course in Chicago, IL

    It is known by many monikers that reflect the feelings and views about a historical and modern Chicago. The best-known is the Windy City. The city distinguishes itself as having one of the biggest and most wide-ranging collections of Impressionist and Post-Impressionist paintings after Musee d?Orsay in Paris. Incorporated as a city in 1837, Chicago has experienced rapid growth and today, it is an international zone for finance, commerce, technology, industry, telecommunications, and transportation. Chicago?s O-Hare International Airport is the busiest airports in the world. Today, the city has grown to be a major world financial center, with the second largest central business district in the US. Home to major financial and futures exchanges such as the Chicago Board Options Exchange and Chicago Stock Exchange, it is also the headquarters of several reputed commercial and retail banks like Chase Bank. Professionals who wish to take a leap ahead in their career would find that they can do well here with certifications such as PRINCE2, PMP, PMI-ACP, CSM, CEH and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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