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Data Science Training Chicago - Using R & Python

  • Looking up to make your career in Data Science with our immersive & Certification-centric Data Science Training in Chicago.
  • Expertise yourself to turn out to be a data concentric company/industry’s leading resource by gaining explicit data science & machine learning skills.
  • Get yourself proficient enough to manage powerful web technologies like Python, R, Spark & Hadoop, for extracting knowledge & insights from data.

Next Class Begins in 6 days - 26 Apr 2024

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Why Data Science Certification Training in Chicago?

Here are a few convicting reasons why certification in Data science via Data Science classes Chicago is soaring high.

1000 New Jobs

Chicago will be added by 1000 new jobs in the next five coming years.

1,90,000 Jobs

The US including Chicago leads in the Data Science market and will create 1,90,000 Data Scientists jobs.

Salesforce Growth

You Should Join Our Classes If You Are:

  • Just starting off & aren’t sure where to start from
  • In an established role but need to dive deep
  • Looking to brush up your skills & master the course
  • Willing to get better in your current or new job

11% Growth

The Bureau of Labor Statistics predicts the Data Science field will grow by 11% by 2024.

High Entry-level Pay

As per Glassdoor, Data Science is the highest paying entry-level job in the US including Chicago.

Why Making a Career in Data Science is a Smart Move?

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Get all the technical skills to help businesses transform their big data!

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IT

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Recruitment

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Medicine/Healthcare

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Analytics

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Bank/Finance

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Semiconductors

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Manufacturing

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Energy/utility

Data Scientist

Data Analyst

Data Engineer

Business Intelligence - BI Developer

Some Hilarious & Concealed Facts About Chicago

  • Chicago is the third-largest city in the US and home to an expected 2.7M residents.
  • Chicago loves to endow with a taste of its excellent food along with its summer atmosphere.
  • Chicago is the hometown of the gospel, electric blues, house, juke, footwork, and drill. The exclusive sounds born in Chicago continue to vibrate around the world.
  • The magnificence of Chicago lies not only in its beautiful architecture but also in the city's enormous preserved green and open spaces. Chicagoans also have 580 parks and 8,300 acres of green spaces at their removal.
Chicago is one of the happiest, clean, and most lively hubs in northeastern Illinois. This city has deep modesty and humility in its roots. Chicago has a scope of quick growth with an added advantage of high employment openings. This city is a great spot of food, timely transportation, great tech job opportunities, diversity, reasonably priced street shopping, great street festivals, and beautiful scenery - all in all, a great city to live your purposeful work-balance life.
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Instructor-led Live Online Data Science Classes


Starting
Duration
Price

calendar-icon126 Apr

WEEKDAY - Filling Fast

8 weeks

09.00 - 10.30 PM EST

USD 1499

USD 1274

Flat 15% Off

calendar-icon110 May

WEEKDAY

8 weeks

09.00 - 10.30 PM EST

USD 1499

calendar-icon127 Apr

WEEKEND

6 weeks

08.00 - 11.00 AM EST

USD 1499

calendar-icon124 May

WEEKDAY

8 weeks

09.00 - 10.30 PM EST

USD 1499

calendar-icon107 Jun

WEEKDAY

8 weeks

09.00 - 10.30 PM EST

USD 1499

USD 1199

Flat 20% Off

Early Bird Discount

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Easy Installments

Detail
WEEKDAY - Filling Fast

calendar-icon126 Apr


8 weeks

09.00 - 10.30 PM EST

USD 1499

USD 1274

Flat 15% Off

Enroll Now
WEEKDAY

calendar-icon110 May


8 weeks

09.00 - 10.30 PM EST

USD 1499

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Not Sure Which Data Science Training Chicago Class to Join?  

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Data science Training Course Roadmap

Scroll through the concepts that we cover in our Data Science Course

Data Science With Python OR R Programming

    • What is Data Science
    • Understanding the Data
    • Importance of Data Science
    • How is data science different from BI and Reporting?
    • End to End Data Science Project Life Cycle
    • How does Predictive Analysis Work
    • Primer to R programming
    • What is R? Similarities to OOP and SQL
    • Installation of R and R-studio
    • Types of objects in R – lists, matrices, arrays, data.
    • frames etc.
    • Creating new variables or updating existing variables
    • If statements and conditional loops - For, while etc.
    • String manipulations
    • Subsetting data from matrices and data.frames
    • Casting and melting data to long and wide format
    • Hands-on Assignment, Real Scenarios, MCQs, Practice
    • Tests on IR

Python

    • What is Python?Importance of Python in Data Science
    • Python Installation Guidelines
    • Numerical parameters to represent data
    • NumPy for mathematical computing
    • Scientific computing with Python
    • Text, color map, markers, widths with Matplotlib.
    • Importing and exporting datasets in Python
    • Feature Engineering: Feature Selection and Extraction
    • Practice Tests on Python

Statistical Analysis

    • Statistical parameters to represent data
    • Apply function for parallel processing with Python
    • Statistical and Non-statistical Analysis
    • Population and Sample
    • Statistical Analysis Process,
    • Data Distribution
    • Hands-on Assignment, Real Scenarios, MCQs,
    • Practice Tests on , Statistics
    • Gradient Descent in Boosting Algorithms
    • Multiple linear regression
    • Linear regression vs Logistic regression
    • Understanding NaÃŊve Bayes, Bayes theorem
    • Clustering - K-Means & Hierarchical
    • Hands-on Assignment, Real Scenarios, MCQs,
    • Practice Tests on Machine Learning

Machine Learning

    • Types of Machine Learning
    • What is Supervised, Unsupervised and Reinforcement
    • Decision trees and Random Forest
    • Advantages of tree-based models?
    • Algorithms used in Machine Learning techniques
    • Difference between Data Science, Machine Learning
    • and AI
    • Gradient Descent in Boosting Algorithms
    • Multiple linear regression
    • Linear regression vs Logistic regression
    • Understanding NaÃŊve Bayes, Bayes theorem
    • Clustering - K-Means & Hierarchical
    • Hands-on Assignment, Real Scenarios, MCQs,
    • Practice Tests on Machine Learning

AI,Deep Learning, Natural Language Processing

    • Working with Neural Network
    • Functioning & Usage
    • Convolutional Networks
    • Recurrent Neural Networks
    • AutoEncoders
    • Long Short Term Memory
    • Deep learning with Keras
    • What is TensorFlow?
    • Deep learning with TensorFlow
    • Gradient Descent in Neural Networks
    • Defining and composing models, and deploying
    • TensorBoard.
    • Important Parameters of Perceptron
    • Recursive Neural Tensor Network theory
    • Hands-on Assignment, Real Scenarios, MCQs, Practice
    • Tests on Deep Learning & NLP

Data Science training Certification Course Roadmap

  • What all do we cover in our Data Science Courses

    The Data Science learning path that you get to cover at JanBask Training is very informative and engaging. It has been prepared after vigorous market research on the trends of Data Science, industry needs, etc. Take a look at the things that we cover in this course.

    • What is Data Science?
    • Data Science Life Cycle
    • What is Machine Learning?
    • What is Business Analytics?
    • What is Artificial Intelligence?
    • How is data science different from BI and Reporting
    • End to End Data Science Project Life Cycle
    • knowledge of the concepts of data collection & data mining.
    • Why R and importance of R in Analytics
    • Installation of R and R-studio
    • Working Directories
    • Data Types, Operators, Loops-For and While
    • If-else statements, Nested statements
    • Working with Vector and Matrices
    • Reading and Writing Data in R
    • Working with Data, Manipulating Data
    • Objects, and Vectors
    • Why Python for data science?
    • Overview of Python- Starting with Python
    • Installation of Python
    • Python Editors & IDE’s
    • Understand Jupyter notebook & Customize Settings
    • Concept of Packages/Libraries
    • Installing & loading Packages & Name Spaces
    • Data Types & Data objects/structures
    • List and Dictionary Comprehensions
    • Control flow & conditional statements
    • Definition and computation of the probability
    • Measurement of central tendencies and its applications
    • Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson)
    • Various types of probability distributions(Continuous and discrete)
    • Measures of Central Tendencies and Variance
    • Sampling and Sampling distributions
    • Measures of shape( Skewness and Kurtosis)
    • Measures of the relationship between variables(Correlation, causation)
    • Hypothesis Testing(t-test, Chi-square, Anova)
    • Measures of Dispersion( Variance, std. deviation, Range)
    • Prediction and Confidence interval-Computation and Analysis
    • Correlation, Covariance, and Causation
    • Supervised Learning
    • Algorithms in Supervised learning
    • Regression & Classification
    • Regression vs classification
    • Computation of correlation coefficient and Analysis
    • Multivariate Linear Regression Theory
    • Coefficient of determination (R2) and Adjusted R2
    • Model Misspecifications
    • Economic meaning of a Regression Model
    • Bivariate Analysis
    • Naive Baye classifier, Model Training
    • ANOVA (Analysis of Variance)
    • What is Clustering
    • Supervised vs Unsupervised learning
    • Data Mining Process
    • Measure of distance
    • Hierarchical Clustering / Agglomerative Clustering
    • Non-clustering, K-Means Clustering
    • dimension reduction
    • Advantages of PCA
    • Calculation of PCA weights
    • Definition of a network (the LinkedIn analogy)
    • The measure of Node strength in a Network
    • What is Market Basket / Affinity Analysis
    • The measure of distance/similarity between users
    • Pre-processing, corpus Document-Term Matrix (DTM) and TDM
    • Why is Deep Learning taking off?
    • Advantage of Deep Learning
    • What is the difference between ML, DL and AI?
    • Why is deep learning important?
    • Sharing Variables
    • Activation Functions
    • Illustrate Perceptron
    • Training a Perceptron
    • Neural Networks with TensorFlow
    • Convolutional Neural Networks (CNN)
    • Convolution and Pooling layers in a CNN
    • Understanding and Visualizing a CNN
    • Recurrent Neural Networks (RNN)
    • What is Natural Language Processing?
    • What Can Developers Use NLP Algorithms For?
    • Open Source NLP Libraries
    • Topic Modeling
    • Sentiment Extraction
    • Lexicons and Emotion Mining
    • Hadoop Installation and Setup
    • Hadoop ecosystem components
    • Hadoop’s Key Characteristics
    • What is Big Data & its analytics
    • Hadoop Ecosystem and HDFS
    • Hadoop Core Components
    • Hadoop Cluster and its Architecture
    • Rack Awareness and Block Replication
    • YARN and its Advantage
    • MapReduce Framework and Pig
    • Apache Spark Next-Generation Big Data Framework
    • How Spark differs from other frameworks?
    • What is Scala
    • Scala in other Frameworks
    • Introduction to Scala REPL
    • Basic Scala Operations
    • Variable Types in Scala
    • Control Structures in Scala
    • Understanding the constructor overloading,
    • Various abstract classes
    • The hierarchy types in Scala,
    • Foreach loop, Functions and Procedures
    • Collections in Scala- Array
    • Overview to Spark
    • Spark installation, Spark configuration,
    • Spark Components & its Architecture
    • Spark Deployment Modes
    • Limitations of MapReduce in Hadoop
    • Working with RDDs in Spark
    • Introduction to Spark Shell
    • Deploying Spark without Hadoop
    • Parallel Processing
    • Spark MLLib - Modelling BigData with Spark
    • what is Kafka, Why Kafka,
    • Configuring Kafka Cluster
    • Kafka architecture
    • Producing and consuming messages
    • Operations, Kafka monitoring tool
    • Need of Apache Flume
    • What is Apache Flume
    • Understanding the architecture of Flume
    • Basic Flume Architecture
    • What is Data Visualization
    • Overview to Tableau 10.0
    • Installing Tableau, Establishing Connection
    • Tableau interface
    • Connecting to DataSource
    • Installation of Tableau Desktop
    • Architecture of Tableau
    • Connection to Excel, cubes, and PDFs
    • Data extraction, Data blending
    • Calculations to your workbook
    • Mapping data in Tableau
    • Custom Geocoding, Polygon Maps.
    • Web Mapping Services.
    • Background Images.
    • Dashboards and Stories

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  • Understand the real world scenarios with real-time job oriented projects and case studies
  • Learn from World Class Trainers who are one amongst the top rated working IT Professionals
  • Clear your certifications while we make you ready for the huge job market present out there

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