Data Scientist Certification Program in Houston

Got 30 LPA Package | Fresher to Data Engineer

Intellipaat helped me to acquire a solid job in the third year of BTech. I received seven job offers, with 30 LPA as the highest CTC. Thanks to Intellipaat for making my career successful.

Fresher

Data Engineer

Gaurav Saboo

Senior Technical Associate | India

Salary Hike

I got promoted to Sr. Technical Associate with a 60% hike after completing this course from Intellipaat. The training taught me new skills and how to implement the same in my current role.

Technical Analyst

Senior Technical Associate

Melwin Rodrigues

Data Scientist | India

Non-Tech to Data Scientist

Becoming a Data Scientist from a Customer Service Agent was possible only due to expert guidance by Intellipaat trainers. Even after working for 10 years in customer care, I am a Data scientist today.

Customer Service Agent

Data Scientist

D Rajesh Sahoo

Machine Learning Engineer | India

Civil Engg Graduate to Machine Learning Engg.

Intellipaat made my transition from civil engineering graduate to IT industry professional smoothly with its round-the-clock support, excellent curriculum, industry-standard projects, and efficient trainers.

B. Tech — Civil Engineering

Machine Learning Engineer

Ankit Kumar

Data Scientist | Delhi

Career Transition Handbook

B.Com Graduate to Data Scientist

Intellipaat has given me the confidence that anyone can become a Data Scientist with its rich course and expert guidance. With the help of Intellipaat, I switched from a non-tech role to a Data Scientist.

B.com Graduate

Data Scientist

Mahesh Chowdary

Data Scientist | Hyderabad

Non-Tech to Tech

Today, I am a certified Data Scientist and this has happened post I enrolled in this program. It covers all the necessary concepts and techniques in lucid language. I am thankful to Intellipaat for giving me this career growth.

Aircraft Maintenance Engineer

Data Scientist

Jeanette Masso

Big Data Developer | Dallas

60% Hike

The course helped me make a career transition from Computer Technical Specialist to Big Data developer with a 60% hike. The online interactive sessions by trainers are the best thing about Intellipaat.

Computer Technical Specialist

Big Data Developer

Pratik Kumar

Product Manager | India

85% Salary Hike

With this course, I switched to the Product Manager role with an 85% hike. They conducted so many interviews, live capstone projects, and resume-building strategies that gave me confidence.

Marketing Manager

Product Manager

Prosenjeet Saha

Data Scientist | Bengaluru

Manager to Data Scientist

Intellipaat offered me the best learning experience, and today, I have successfully transitioned from a Manager in Edutech to a Data Scientist. The comprehensive curriculum and the career guidance sessions helped me a lot.

Manager Analytics & Data Science

Data Scientist

Sahas Barangale

Program Manager | Pune

Consultant to Program Manager

Thanks to Intellipaat, I was able to switch to the role of a Program Manager from a Microsoft Dynamics Consultant. Gaining knowledge in the latest technologies as per industry standards helped me the most.

Microsoft Dynamics Consultant

Program Manager

Bhawna Kaliraman

Data Science Intern | Gurugram

Lecturer to Data Scientist

I was able to achieve my goal of becoming a Data Science professional from being a lecturer, due to Intellipaat. It offered a platform to learn Data Science from the best trainers. The real-life projects were good.

Lecturer

Data Science Intern

55% Average Salary Hike

$1,20,000 The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Data Science Course Curriculum

Live Course Self Paced

Module 1 – Preparatory Session - Linux and Python

Python

Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.
Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.

Linux

Introduction to Linux – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS.
Linux Basics – File Handling, data extraction, etc.
Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.

Module 2 – Data Analysis With MS-Excel

Excel Fundamentals

Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
VBA Macros, Ranges and Worksheet in VBA
IF conditions, loops, Debugging, etc.

Excel For Data Analytics

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

Charts, Pie charts, Scatter and bubble charts
Bar charts, Column charts, Line charts, Maps
Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools

Power Pivot, Power Query and Power View

Classification Problems using Excel

Binary Classification Problems, Confusion Matrix, AUC and ROC curve
Multiple Classification Problems

Information Measure in Excel

Probability, Entropy, Dependence
Mutual Information

Regression Problems Using Excel

Standardization, Normalization, Probability Distributions
Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.

Module 3 – Data Wrangling with SQL

SQL Basics –

Fundamentals of Structured Query Language
SQL Tables, Joins, Variables

Advanced SQL –

SQL Functions, Subqueries, Rules, Views
Nested Queries, string functions, pattern matching
Mathematical functions, Date-time functions, etc.

Deep Dive into User Defined Functions

Types of UDFs, Inline table value, multi-statement table.
Stored procedures, rank function, triggers, etc.

SQL Optimization and Performance

Record grouping, searching, sorting, etc.
Clustered indexes, common table expressions.

Hands-on exercise:

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Module 4 – Python With Data Science

Extract Transform Load

Web Scraping, Interacting with APIs

Data Handling with NumPy

NumPy Arrays, CRUD Operations, etc.
Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

Data Preprocessing

Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

Data Visualization

Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
Regression plots, categorical plots, area plots, etc, with Python seaborn.

Module 5 – Linear Algebra and Advanced Statistics

Descriptive Statistics –

Measure of central tendency, measure of spread, five points summary, etc.

Probability

Probability Distributions, bayes theorem, central limit theorem.

Inferential Statistics –

Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Module 6 – Machine Learning

Introduction to Machine learning

Supervised, Unsupervised learning.
Introduction to scikit-learn, Keras, etc.

Regression

Introduction classification problems, Identification of a regression problem, dependent and independent variables.
How to train the model in a regression problem.
How to evaluate the model for a regression problem.
How to optimize the efficiency of the regression model.

Classification

Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
How to train the model in a classification problem.
How to evaluate the model for a classification problem.
How to optimize the efficiency of the classification model.

Clustering

Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
How to train the model in a clustering problem.
How to evaluate the model for a clustering problem.
How to optimize the efficiency of the clustering model.

Supervised Learning

Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
Random Forest – Creating random forest models for classification problems in a supervised learning approach.
Support Vector Machine – SVM or support vector machines for regression and classification problems.
Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning

K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
r2, adjusted r2, mean squared error, etc.

Module 7 – Deep Learning Using TensorFlow

Artificial Intelligence Basics

Introduction to keras API and tensorflow

Neural Networks

Neural networks
Multi-layered Neural Networks
Artificial Neural Networks

Deep Learning

Deep neural networks
Convolutional Neural Networks
Recurrent Neural Networks
GPU in deep learning
Autoencoders, restricted boltzmann machine

Module 8 – Power BI

Power BI Basics

Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX

Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

Slicers, filters, Drill Down Reports
Power BI Query, Q & A and Data Insights
Power BI Settings, Administration and Direct Connectivity
Embedded Power BI API and Power BI Mobile
Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

Module 9 – Deploying Machine Learning Models With Cloud

Introduction to MLOps

MLOps lifecycle
MLOps pipeline
MLOps Components, Processes, etc

Deploying Machine Learning Models

Introduction to Azure Machine Learning
Deploying Machine Learning Models using Azure

Module 10 – GIt

Version Control

What is version control, types, SVN.

GIT

Git Lifecycle, Common Git commands, Working with branches in Git
Github collaboration (pull request), Github Authentication (ssh and Http)
Merging branches, Resolving merge conflicts, Git workflow

Module 11 – Data Science Capstone Project

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:

Extracting, loading and transforming data into usable format to gather insights.
Data manipulation and handling to pre-process the data.
Feature engineering and scaling the data for various problem statements.
Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
Assessment and monitoring of the model created using the machine learning models.

Module 12 - Business Case Studies

Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.

Module 13 – Natural Language Processing

Text Mining, Cleaning, and Pre-processing

Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text classification, NLTK, sentiment analysis, etc

Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine

Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.

Module 14 – Computer Vision

RBM and DBNs & Variational AutoEncoder

Introduction rbm and autoencoders
Deploying rbm for deep neural networks, using rbm for collaborative filtering
Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

Constructing a convolutional neural network using TensorFlow
Convolutional, dense, and pooling layers of CNNs
Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

Automated conversation bots leveraging
Generative model, and the sequence to sequence model (lstm).

Distributed & Parallel Computing for Deep Learning Models

Parallel Training, Distributed vs Parallel Computing
Distributed computing in Tensorflow, Introduction to tf.distribute
Distributed training across multiple CPUs, Distributed Training
Distributed training across multiple GPUs, Federated Learning
Parallel computing in Tensorflow

Reinforcement Learning

Mapping the human mind with deep neural networks (dnns)
Several building blocks of artificial neural networks (anns)
The architecture of dnn and its building blocks
Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
Deploying deep learning models in Serverless Environments
Deploying Model to Sage Maker
Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Module 15 – Data Science At Scale with Pyspark

Introduction to Big Data And Spark

Apache spark framework, RDDs, Stopgaps in existing computing methodologies

RDDs

RDD persistence, caching, General operations: Transformation, Actions, and Functions.
Concept of Key-Value pair in RDDs, Other pair, two pair RDDs
RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts
RDD Partitioning & How it Helps Achieve Parallelization

Advanced Concepts & Spark-Hive

Passing Functions to Spark, Spark SQL Architecture, SQLContext in Spark SQL
User-Defined Functions, Data Frames, Interoperating with RDDs
Loading Data through Different Sources, Performance Tuning
Spark-Hive Integration