Data Science Training Chicago Details
You will complete this course within 40hours.
we will provide for the students fast track & Weekend Batches at your flexible timings.
Who Should Learn Data Science?
Generally, your journey to becoming a data scientist would be much easier if you possess the following: A solid background in an analytical discipline such as mathematics, physics, computer science, or engineering. A basic understanding of computer programming, e.g. Python and R.
Do the faculty have a real-time experience?
Yes, the faculty has more than 10+ years of experience in real-time.
How many batches does trainer complete for this course?
✓ Successfully trained more than 30 batches
✓ Trained more than 250 learners
Will the trainer concentrate on the practical sessions?
Absolutely, the trainer concentrates on 30% theoretical and 70% on practical.
Do the faculty clear all the doubts during the session?
Obviously, the faculty clears all the doubts during the session.
- Limitations of Machine Learning
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis
- What is Data Mining
- Value Chain
- Types of Analytics
- Lifecycle Probability
- Analytics Project Lifecycle
- Advantage of Deep Learning over Machine learning
- Reasons for Deep Learning
- Real-Life use cases of Deep Learning
- Review of Machine Learning
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data & Sources
- Data Quality & Changes
- Data Quality Issues
- Data Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs. OLAP
- How is Data Stored?
- What is Big Data?
- 5 Vs of Big Data
- Big Data Architecture
- Big Data Technologies
- Big Data Challenge
- Big Data Requirements
- Big Data Distributed Computing & Complexity
- Map Reduce Framework
- Hadoop Ecosystem
- What Data Science is
- Why Data Scientists are in demand
- What is a Data Product
- The growing need for Data Science
- Large Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases
- Data Science Project Life Cycle & Stages
- Data Acquisition
- Where to source data
- Evaluating input data
- Data formats
- Data Quantity
- Data Quality
- Resolution Techniques
- Data Transformation
- File format Conversions
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc.
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Using Variables
- Built-in Functions
- Strings Different Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control.
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
- The xrange() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets
- Learning NumPy
- Introduction to Pandas
- Creating Data Frames
- Grouping Sorting
- Plotting Data
- Creating Functions
- Slicing/Dicing Operations
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections
- Classes & OOPs
- What is Statistics
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing
- Confidence Level
- Degrees of freedom
- what is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs Regression
- Uses of Correlation & Regression
MACHINE LEARNING, DEEP LEARNING & AI USING PYTHONIntroduction
- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
- Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures
- Creating predictive models
- Understanding K-Means Clustering
- Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Recommendation Use-case
- Case study
- How to build Decision trees
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Decision Tree
- Confusion Matrix
- Case stud
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Case study
- Case study
- Various approaches to solve a Data Science Problem
- Pros and Cons of different approaches and algorithms.
- Case study
- Introduction to Predictive Modeling
- Linear Regression Overview
- Simple Linear Regression
- Multiple Linear Regression
- Case study
- Logistic Regression Overview
- Data Partitioning
- Univariate Analysis
- Bivariate Analysis
- Multicollinearity Analysis
- Model Building
- Model Validation
- Model Performance Assessment AUC & ROC curves
- Case Study
- Introduction to SVMs
- SVM History
- Vectors Overview
- Decision Surfaces
- Linear SVMs
- The Kernel Trick
- Non-Linear SVMs
- The Kernel SVM
- Describe Time Series data
- Format your Time Series data
- List the different components of Time Series data
- Discuss different kind of Time Series scenarios
- Choose the model according to the Time series scenario
- Implement the model for forecasting
- Explain working and implementation of ARIMA model
- Illustrate the working and implementation of different ETS models
- Forecast the data using the respective model
- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential Smoothing model can be applied
- Implement respective model for forecasting
- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series forecasting
- Forecasting for given Time period
- Case Study
- Various machine learning algorithms in Python
- Apply machine learning algorithms in Python
- How to select the right data
- Which are the best features to use
- Additional feature selection techniques
- A feature selection case study
- Preprocessing Scaling Techniques
- How to preprocess your data
- How to scale your data
- Feature Scaling Final Project
- Highly efficient machine learning algorithms
- Bagging Decision Trees
- The power of ensembles
- Random Forest Ensemble technique
- Boosting – Adaboost
- Boosting ensemble stochastic gradient boosting
- A final ensemble technique
- Introduction Model Tuning
- Parameter Tuning GridSearchCV
- A second method to tune your algorithm
- How to automate machine learning
- Which ML algo should you choose
- How to compare machine learning algorithms in practice
- Sentimental Analysis
- Case study
- Introduction to Spark Core
- Spark Architecture
- Working with RDDs
- Introduction to PySpark
- Machine learning with PySpark – Mllib
DEEP LEARNING & AI USING PYTHONDeep Learning & AI
- Case Study
- Deep Learning Overview
- The Brain vs Neuron
- Introduction to Deep Learning
- The Detailed ANN
- The Activation Functions
- How do ANNs work & learn
- Gradient Descent
- Stochastic Gradient Descent
- Understand limitations of a Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- Building a multi-layered perceptron for classification
- Why Deep Networks
- Why Deep Networks give better accuracy?
- Use-Case Implementation
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
- Convolutional Operation
- Relu Layers
- What is Pooling vs Flattening
- Full Connection
- Softmax vs Cross Entropy
- ” Building a real world convolutional neural network for image classification”
- Recurrent neural networks rnn
- LSTMs understanding LSTMs
- long short term memory neural networks lstm in python
- Restricted Boltzmann Machine
- Applications of RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Building a Autoencoder model
- Introducing Tensorflow
- Introducing Tensorflow
- Why Tensorflow?
- What is tensorflow?
- Tensorflow as an Interface
- Tensorflow as an environment
- Computation Graph
- Installing Tensorflow
- Tensorflow training
- Prepare Data
- Tensor types
- Loss and Optimization
- Running ensorflow programs
- Tensorflow data types
- CPU vs GPU vs TPU
- Tensorflow methods
- Introduction to Neural Networks
- Neural Network Architecture
- Linear Regression example revisited
- The Neuron
- Neural Network Layers
- The MNIST Dataset
- Coding NIST NN
- Deepening the network
- Images and Pixels
- How humans recognise images
- Convolutional Neural Networks
- ConvNet Architecture
- Overfitting and Regularization
- Max Pooling and ReLU activations
- Strides and Zero Padding
- Coding Deep ConvNets demo
- Debugging Neural Networks
- Visualising NN using Tensorflow
- Transfer Learning Introduction
- Google Inception Model
- Retraining Google Inception with our own data demo
- Predicting new images
- Transfer Learning Summary
- Extending Tensorflow
- Keras vs TFLearn Comparison
What you will get!
The trainer will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.
- Sure, We will share all classes High-quality Pre-Recording videos.
- You get Free Access for Bundle of videos. It will help you learn the next version of this technology.
A virtual classroom is an online learning environment that allows Instructors and students to communicate, interact, collaborate, and share knowledge of Data Science Training Chicago.
In the Self-Paced Online Course, Students can take the time they need and set their suitable schedule. Self-paced courses do not follow a set schedule. Course materials are entirely available as soon as the course begins.
Self-paced learning has several advantages that will enhance the performance of the students.
- No time pressure
- No need for a schedule
- Improves memory
- Suitable for different learning styles
Choice of Training Mode
Instructor Led TrainingDuration: 30-40 hours
Real time Scenarios
Free Bundle Life time Access
100% Hands-on Classes
Instructor Led Live Online Classes
Instant Doubt Clarification
Data Science Training VideosDuration: 35+ hours
Free Bundle Access
Course Future Updates
No Doubt Clarification
Data Science Training Chicago Course FAQ!
What if I miss a class?
We record each LIVE class session you undergo through and we will share the recordings of each session/class.,
If I cancel my enrollment, will I get the refund?
If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within 48 hours of initial registration. Please make a note that refunds will be processed within 30 days of prior request.
Is this a job guarantee program?
Institute doesn’t provide you any guarantee to get a job but as a guide as a mentor, as a friend, we will always provide you a good opportunity.
Do you help me to get a job?
We will definitely help you in attending interviews and it’s your caliber, hard-work, and ability.
After getting job can I expect support from you?
Even after getting a job when you stuck in any problem we will help but not for sure.
How long they give support after the course?
We are ready to help always, however it should fit in our course curriculum.
Do you Provide any real-time projects in CV Preparation?
In CV Preparation for real-time projects provide template documentation for the implementation project. You can access practice that in your idea system. If you have any difficulty, you can approach the trainer.
Will you provide any sample CV’S?
Yes, we will provide sample C.V’s for experience 2-3 years.
Can I expect your helping hand in C.V Preparation?
Definitely, we help you during C.V Preparation.
In what way you help me in interview preparation?
Yeah, Even after the completion of course, we will provide you some interview questions where you can concentrate on them.
What will they provide for references for interview preparation?
We provide excellent study materials and customizable course curriculum to students for superior quality training and we also provide videos recordings which support throughout your career.
Can I crack the interview with 3 year’s experience?
At the completion of this course, with hands-on training definitely give you confidence that you go in an interview with leaving 3 years of experience.
Data Science is in full swing as of now. That implies there will be an enormous flood of supply as ‘everybody does it”. Much the same as entering the market when it’s at its high is definitely not a decent arrangement, it’s likewise difficult to join a field that is in design.
What is the certification procedure?
Yes during the course, we will guide you and give you a clear picture about certification procedure.
Can i attempt certification after this course?
All the topics will be covered during the course. We provide question banks which will be helpful for you to attempt for certification.
Do you support me to pass certification?
Even after completion, of course, you can approach a trainer if you have any doubt regarding certification.
Do they provide the certification sample queries and references?
Surely, We provide the certification sample queries and references.
Does this certification benefit my career?
The certification has multiple impacts and encouraging factors in the recruitment of experts. Organizations claim that hiring certified professionals has served them with greater deployment and yield on investment. Check Cloud Career Building Up Force for New-age IT Professionals to understand the career benefits.
A Data Scientist, as per Harvard Business Review, “is a high-positioning proficient with the preparation and interest to make revelations in the realm of Big Data”. Thusly it shocks no one that Data Scientists pine for experts in the Big Data Analytics and IT industry.
With specialists foreseeing that 40 zettabytes of information will be in presence by 2020 (Source), Data Science profession openings will just shoot through the rooftop! The deficiency of gifted experts in a world that is progressively going to information for basic leadership has likewise prompted the immense interest for Data Scientists in new companies just as entrenched organizations. A McKinsey Global Institute study expresses that by 2018, the only us will confront a deficiency of around 190,000 experts with profound scientific abilities. With the Big Data wave giving no indications of backing off, there’s a surge among worldwide organizations to enlist Data Scientists to tame their business-basic Big Data.
Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
Data Science is about the extraction of crude and quantitative information into composed and useful data with the goal that it tends to be examined, pictured, and kept up for records. It is a blend of some hard abilities (like learning Python and SQL) and delicate aptitudes (like business abilities or relational abilities) and so on.
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Python is one of the most well-known programming dialects for elevated level information preparing, because of its basic linguistic structure, simple lucidness, and simple cognizance. Python’s expectation to absorb information is low, and because of its numerous information structures, classes, settled capacities and iterators, other than the broad libraries, this language is the primary selection of information researchers for breaking down, extricating data and settling on educated business choices through huge information.
This Data Science for Python programming course is an umbrella course covering significant Data Science ideas like exploratory information examination, insights essentials, speculation testing, relapse characterization displaying systems and AI algorithms. Extensive hands-on labs and a meeting prep will assist you with finding worthwhile occupations.
What is a live virtual classroom?
A virtual classroom is an online learning environment that allows Instructor and students to communicate, interact, collaborate, and share knowledge.
What are the benefits of online training?
Online training is a valuable and cost-effective way for Employees and Students. Online training courses are of two kinds – free and paid versions. Here are a few advantages of online training that showcase how it helps improve an employee’s professional life:
- Convenience and flexibility
- Comfortable learning environment
- Career advancement
- More choice of course topics
- Improve your technical skills
- Easy Accessibility
- It speeds up career progression
Is online courses Good or bad?
Of course, It is good. One of the significant benefits of the online course is its much affordable cost of learning. Also, it saves a student’s energy & time.
What is online training for employees?
Live Virtual Classroom has several advantages. Online Training allows employees to learn at their own pace and a convenient time for them. In other words, they are suitable for self-paced Training also.