Data Science Training Program Online in Chicago

Comprehensive Data Science Training in Chicago

SynergisticIT offers comprehensive Data Science training in Chicago that takes you from Data Science fundamentals to advanced level concepts in just 5 to 6 months. Under the end-to-end assistance of our live instructors, you will learn to derive valuable insight from complex data. Our immersive training enables you to learn Data Science principles by working on real-life projects and case studies. After completing our Data Science training in Chicago, you will be able to create robust predictive models, automate tasks, apply Python programming to clean data, manipulate the database, and perform data analysis of different types. Equip yourself with some next-level skills required for a successful data-driven career by joining the best Data Science training bootcamp in Chicago.

Benefits of Learning Data Science Training

  • Once you get upskilled through advanced Data Science training in Chicago, you will be rewarded a certification that can increase your career prospects and give you a competitive edge. It can also improve your earning potential as certified Data Science professionals get higher salaries than non-certified ones.

  • These days, almost every industry has recognized the importance of organizing and interpreting data to bring out useful information for business growth. This is where the need for skilled Data Scientists emerges. Thus, you can generate an ample number of jobs for yourself by learning Data Science. If your dream is to work in a Fortune 500 Company like Apple, Facebook, or PayPal, Data Science training in Chicago is your way to go.

  • Enrolling in a Data Science Bootcamp is a prudent decision to stay ahead in the industry. You will learn all the necessary skills, tools, latest trends, and best Data Science practices under the updated curriculum of immersive training.

Data Science Training Bootcamp in Chicago
  • Data Science is an extensive multidisciplinary field that cannot be excelled singlehandedly via free online videos or reading blogs. When you want to build a Data Science Career, you need to loosen your pockets and sign up for Data Science training in Chicago. Instructor-led training provides a structured program that covers each topic systematically. It can be beneficial to develop a solid understanding of Data Science from foundation to advanced level.

  • A self-learning approach to master Data Science principles is not efficient. You need to get hands-on training to develop a practical knowledge base for applying theoretical concepts to real-world business problems.

Skills you will Learn in our Data Science Training

Our Data Science training in Chicago starts from the basics. Once the course progresses, you will deep dive into more advanced topics such as web scraping, predictive modeling, data visualization, AI, ML, deep learning, NLP, etc. This task-based training allows you to hone your Data Science skills through in-class project work, assignments, and daily assessments. We also provide career coaching and take regular mock tests to determine your knowledge gaps right on time. This training is curated for those aspirants who want to gain Data Science fluency.

Introduction to Data Science with Python

  • What is Data Science & Analytics?
  • Common Terms in Analytics
  • What is Data & its Classification?
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem-solving framework
  • List of steps in Analytics projects
  • Build Resource plan for analytics project
  • Finding the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • How leading companies are harnessing the power of analytics?
  • Why Python for data science?

Python Introduction & Data Structures

  • Python Tools & Technologies
  • Benefits of Python
  • Important packages (Pandas, NumPy, SciPy, Scikit-learn, Seaborn, Matplotlib)
  • Why Anaconda?
  • Installation of Anaconda & other Python IDE
  • Python Objects, Numbers & Booleans, Strings, Container Objects, Mutability of Objects
  • Jupyter Notebook
  • Data Structures
  • Python Practical Session / Task

Numerical Python (NumPy)

  • Data Science and Python
  • What is NumPy?
  • NumPy Operations
  • Types of Arrays
  • Basic Operations
  • Indexing & Slicing
  • Shape Manipulation
  • Broadcasting
  • NumPy Practical Session / Task

Pandas Data Analysis

  • Why Pandas?
  • Pandas Features
  • Pandas File Read & Write Support
  • Data Structures
  • Understanding Series
  • Data Frame
  • Pandas Practical Session / Task Data Standardization
  • Missing Values
  • Data Operations
  • NumPy Practical Session / Task

Matplotlib & Seaborn Data Visualization

  • What is Data Visualization?
  • Benefits & Factors of Data Visualization
  • Data Visualization Considerations & Libraries
  • Data Visualization using Matplotlib
  • Advantages of Matplotlib
  • Data Visualization using Seaborn
  • What is a Plot and its types?
  • How to Plot with (x,y)?
  • How to Control Line Patterns and Colors
  • How to Implement Multiple Plots?
  • Matplotlib Practical Session / Task

Data Manipulation: Cleansing – Munging

  • Data Manipulation steps (Sorting, filtering, merging, appending, derived variables, etc)
  • Filling the missing values by using Lambda function and Skewness.
  • Cleansing Data with Python

Data Analysis: Visualization Using Python

  • Introduction exploratory data analysis
  • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas, etc)
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Descriptive statistics, Frequency Tables & summarization

Introduction to Artificial Intelligence (AI) & Machine Learning (ML)

  • What is Artificial Intelligence & Machine Learning?
  • What is Big Data?
  • Understanding the difference between Artificial Intelligence, Machine Learning & Deep Learning
  • Artificial Intelligence in Real World-Applications

Machine Learning Techniques & Algorithms

  • Types of Machine Learning
  • Machine Learning Algorithms
  • Hyper parameter optimization
  • Hierarchical Clustering
  • Implementation of Linear Regression
  • Performance Measurement
  • Principal component Analysis
  • How Supervised & Unsurprised Learning Model Works?
  • Machine Learning Project Life Cycle & Implementation
  • What is Scikit Learn, Regression Analysis, Linear Regression?
  • Difference between Regression & Classification
  • What is Logistic Regression and its implementation?
  • Best Machine Learning Approach

Decision Tree and Random Forest Algorithm

  • What is a Decision Tree and how it works?
  • What is Entropy, Information Gain, Decision Node?
  • In-depth study of Random Forest and understanding how it works?

Naive Bayes and KNN Algorithm

  • What is Naïve Bayes?
  • Advantages & Disadvantages of Naïve Bayes
  • why KNN?
  • Practical Implementation of Naïve Bayes
  • What is KNN and how does it work?
  • How do we choose K?
  • Practical Implementation of KNN Algorithm

Support Vector Machine Algorithm

  • What is Support Vector Machine (SVM)?
  • How Does SVM Work?
  • Applications of SVM
  • Why SVM?
  • Practical Implementation of SVM

Model Deployment & Tableau

  • Flask Introduction & Application
  • Django end to end
  • Working with Tableau
  • Data organisation
  • Creation of parameters
  • Advanced visualization
  • Dashboard data presentation

Introduction to Statistics

  • Descriptive Statistics
  • Sample vs Population Statistics
  • Random variables
  • Probability distribution functions
  • Expected value
  • Normal distribution
  • Gaussian distribution
  • Z-score
  • Central limit theorem
  • Spread and Dispersion
  • Hypothesis Testing
  • Z-stats vs T-stats
  • Type 1 & Type 2 error
  • Confidence Interval
  • ANOVA Test
  • Chi Square Test
  • T-test 1-Tail 2-Tail Test
  • Correlation and Co-variance

Introduction to Predictive Modelling

  • The concept of model in analytics and how to use it?
  • Different Phases of Predictive Modelling
  • Popular Modelling algorithms
  • Different kinds of Business problems - Mapping of Techniques
  • Common terminology used in Modelling & Analytics process

Data Exploration for Modelling

  • Visualize the data trends and patterns
  • Identify missing data & outliers’ data
  • EDA framework for exploring the data & identifying problems with the data by the help of pair plot.
  • What is the need for structured exploratory data?

Data Preparation

  • Merging
  • Normalizing the data
  • Feature Engineering
  • What is the need for Data preparation?
  • Aggregation/ Consolidation - Outlier treatment - Flat Liners - Missing Values-Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • Feature Selection
  • Feature scaling using Standard Scaler
  • Label encoding

Ensemble Learning Techniques

  • In-depth study of Ensemble Learning with Real Examples
  • How to Reduce Model Errors with Ensembles
  • Understanding Bias and Variance
  • Different Types of Ensemble Learning Methods
  • Feature Selection
  • Feature scaling using Standard Scaler
  • Label encoding

Web Scraping using Python Beautiful Soup

  • What is Web Scraping & Why Web Scraping?
  • Web Scraping using Beautiful Soup Practical Session / Task
  • Difference Between Web Scraping Software Vs. Web Browser
  • Web Scraping using Beautiful Soup Practical Session / Task
  • Web Scraping Considerations & Tools
  • Why Beautiful Soup?
  • Common Data & Page Formats on the Web
  • Practical Implementation of Web Scraping
  • Web Scraping Process
  • What is a Parser?
  • Importance of Parsing
  • What are the various Parsers?
  • How to Navigate the Parsers?
  • How to take Output – Printing & Formatting

Time Series Analysis

  • Why Time Series Analysis?
  • What is Time Series?
  • Time Series Components (Seasonality, Trend, Level & Cyclicity) and Decomposition
  • Classification of Techniques like Pattern based or Pattern less
  • Basic to Advance level Techniques (Averages, AR Models, Smoothening, ARIMA, etc)
  • Use Cases of Time Series Analysis
  • When Not to Use Time Series Analysis?
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
  • Time Series Analysis Case Study - Practical Session / Task

Deep Learning

  • What is deep learning
  • The neuron
  • How do neural networks work?
  • Back propagation
  • ANN in Python
  • What are convolutional neural networks?
  • Installing Tensor Flow & Keras
  • CNN in Python
  • Activation function & Epoch

Natural Language Processing (NLP) & Text Mining

  • What is Natural Language Processing (NLP) & Why NLP?
  • NLP with Python
  • Sentiment analysis
  • Bags of words
  • Stemming
  • Tokenization
  • What is Text Mining?
  • Text Mining & NLP
  • Benefits, Components, Applications of NLP
  • NLP Terminologies & Major Libraries
  • NLP Approach for Text Data
  • What is Sentiment Analysis?
  • Steps for Sentiment Analysis
  • Sentiment Analysis Case Study - Practical Session / Task
  • Practical Implementation of NLP
  • NLP Case Study - Practical Session / Task

Market Basket Analysis

  • What is Market Basket Analysis & how it is used?
  • What is Association Rule Mining?
  • What is Support, Confidence & Lift
  • An Example of Association Rules
  • Market Basket Analysis Case Study - Practical Session / Task
Data Science Training in Chicago

Who can take our Online Data Science Training

Our Data Science training in Chicago is designed for:

Professionals with a mathematical, logistics, or analytical background

Statisticians, Economists, and Mathematicians

Software developers or programmers

Aspiring Data Scientists or Business Analysts

Individuals working on Business Intelligence, data warehousing, and reporting tools

Beginners who want to improve their critical thinking abilities and analytical skills

Career Options After Data Science Training

Data Science is a gateway to a splendid number of jobs. Here are some lucrative careers you can consider after completing Data Science training in Chicago:

Data Engineer ($125,732)

BI Solutions Architect ($120,539)

Data Scientist ($120,103)

Business Intelligence (BI) Engineer ($117,044)

Analytics Manager ($112,467)

Data Visualization Developer ($105,501)

Big Data Engineer ($103,092)

Statistician ($97,643)

BI Specialist ($90,286)

Business Analytics Specialist ($84,601)

Highest Paying Data Science Jobs in Chicago

Jump Start your Data Science career at SynergisticIT, the most trusted Data Science Bootcamp in Chicago. We have helped thousands of candidates secure rewarding Data Science jobs in leading tech companies. Our experienced trainers encourage each candidate to deepen their expertise by working on live Data Science projects, which further prepare them for future job challenges. Learn from our industry experts and achieve your career goals today.

Start acquiring valuable Data Science and Data Analyst skills by training at the best online Data Science Bootcamp. Create a robust work portfolio to demonstrate your abilities in the field with the assistance of experienced mentors. Let’s help you achieve your career goals. SynergisticITHome of the Best Data Scientists and Software Programmers in the Bay Area!

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Google Reviewer

Being an international student in USA and realizing that I was on the verge of completing my CS degree with not enough experience or skills to crack the interviews I was desperate for some kind of breakthrough. I started looking for a tech Bootcamp which could work with my study schedule and yet offer me…

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Minh Ho

Good place for anyone struggling to find a technology job with bigger name clients. I worked with them for some time like a year back or so and after my experience with them I had upgraded my coding skills to the standards of major it organizations. Synergisticit is in my opinion one of the very…

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Menglee G.

Synergistic IT was the best decision I made for my career. During my time here, I worked on multiple projects and learned a lot of high demand skills for the competitive tech industry. They have amazing trainers who have lots of experience. I would recommend it to anyone who wants to become a professional in…

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