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Data Science Training Course in Denver

5 (591 Ratings)

Intellipaat's Data Scientist course in Denver is an online instructor-led training to help you gain expertise in Machine Learning algorithms, MLOps, PySpark SQL, Tableau etc. with real-time projects and case studies. Get trained by industry experts in this Data Science training and become a certified Data Scientist.

Ranked #1 Data Science Program by India TV

Key Highlights

50+ Live sessions across seven months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Madras Faculty & Industry Practitioners
1:1 with Industry Mentors
3 Guaranteed Interviews by Intellipaat
24*7 Support
No Cost EMI Option

Data Science Course in Denver Overview

What concepts will you learn in Intellipaat’s Data Scientist course in Denver?

  • Bayes theorem
  • Supervised and unsupervised learning
  • Use cases of recommendation engines 
  • Time series analysis and its techniques
  • Introduction to decision trees
  • Fundamentals of Artificial Intelligence
  • Support vector machine (SVM)
  • Statisticians
  • Business Analytics professionals
  • Developers interested in Predictive Analytics
  • Aspirants looking to excel in their careers as Data Scientists

There are no predefined skills required to sign up for our Data Scientist course in Denver. Enthusiasm for solving mathematical and statistical problems is enough to ease your learning process at Intellipaat.

  • In Denver, a yearly salary of US$106,000 is paid to Data Scientists on average – PayScale
  • 311 Data Science jobs are vacant in Denver alone – LinkedIn
  • Data Scientist job vacancies are expected to reach an astonishing 700,000+ in the coming years – IBM
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We are happy to help you 24/7

With data collection, ’the sooner, the better’ is the best answer - The CEO of Yahoo
Everything is going to be connected with data and mediated by software - The CEO of Microsoft
The world is now awash in data, and we can see consumers more cleanly - The Co-founder of PayPal

Career Transition

55% Average Salary Hike

$1,20,000 The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Career Transition Handbook

Meet Your Mentors

Who can apply for the Data Scientist course?

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
  • Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
Who can aaply

What roles does a Data Scientist play?

Data Scientist

Design and implement scalable codes alongside effectively developing high-quality applications.

Analytics and Insights Analyst

Develop solutions for fixing quality issues in the data upon investigating the reported errors in the data.

AI & ML Engineer

Use Lambda functions and API Gateway to integrate Machine Learning models to web apps and deploy models in SageMaker.

Data Engineer & Data Analyst

Perform data cleansing, data transformation, analyze the outcomes and present the insights in reports and dashboards.

Junior Data Scientist

Analyze the operating behavior using advanced statistical techniques and tools. Also, create algorithms with prescriptive and descriptive methods.

Applied Scientist

Derive intelligence for the business products through designing and developing Machine Learning models.

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Skills Covered

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Tools Covered

tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop
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Course Fees

Online Classroom Preferred

  • Live Classes from IIT Madras Faculty
  • Certification from CCE, IIT Madras
  • Job Assistance ( Mock Interviews, Resume Preparation)
  • Guaranteed 3 Interviews by Intellipaat
  • Dedicated Learning Manager
10 Dec

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

17 Dec

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

24 Dec

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

31 Dec

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,492 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Data Science Course Curriculum

Live Course Self Paced

Module 1 – Preparatory Session - Linux and Python

Preview

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

Preview

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.

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).

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.

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.

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.

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 

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.

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

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

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.
  • 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.

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. 

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

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
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Free Career Counselling

We are happy to help you 24/7

Data Science Projects Covered

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends — the possibilities are endless and our community has something for everyone!

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Career Services

Career Services
Resume

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up-skilling objective.

Resume

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

Resume

Mock Interview Preparation

After 80% of the course completion.

Students will go through several mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

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1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, experience, and future career aspirations.

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Assured Interviews

After 80% of the course completion

Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.

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Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Data Science Certification in Denver

What should I do to unlock my Intellipaat certificate?

You can unlock Intellipaat’s certificate in three simple steps:

  1. Complete the Data Science course in Denver along with the given assignments
  2. Work on several industry-based projects and execute the same successfully
  3. Pass the certification exam

The Data Science certification you receive from Intellipaat is valid for your entire lifetime and is recognized by top organizations across the world.

On successfully completing the Data Science course and passing the Data Science training certification exam, you will receive Intellipaat’s Data Science certificate via our Learning Management System. You can download or share your certificate from this through either email or LinkedIn.

Yes. The Data Science certification issued by Intellipaat is industry-recognized. Besides, due to our affiliation with IBM, you will also receive a Data Science course completion certificate from IBM.

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Data Scientist Course in Denver Reviews

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Data Science Training in Denver FAQ’s

Why should I join these Data Science courses in Denver from Intellipaat?

Intellipaat offers an exclusive online Data Science course in Denver with placement for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:

  • A personal mentor to track your progress
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises, assignments, and projects
  • 24/7 learning support
  • Large community of like-minded learners
  • Industry-recognized Intellipaat badge
  • Personalized job support

Intellipaat, one of the leading E-learning institute in Denver provides best courses in R Certification, best ai courses, Machine Learning, Data Analytics, Data Science with Python, Python, Business Analytics and others to help to increase your understanding of your field of work and to validate your skills.

Intellipaat publishes numerous Data Science blogs weekly. The top free resources available for learners are Tutorial, Interview Questions, and everything about Data Science.

At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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