Data Science Masters Course with Python, Machine Learning, Deep Learning & TensorFlow

Online bootcamp to learn Deep Learning Training with TensorFlow

Course Description

Acadgild’s Data Science Masters is an intensive 6 months course that will transform you into a Data Scientist by delivering hands-on experience in Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, TensorFlow, Apache Spark, R and Tableau. Data scientists work with data according to business needs. They are responsible for data analysis. Big data developers design and implement programs that make the analysis possible. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more.   Job Placement Assistance Program Our job placement program offers students one-on-one career counselling... Read More »

Acadgild’s Data Science Masters is an intensive 6 months course that will transform you into a Data Scientist by delivering hands-on experience in Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, TensorFlow, Apache Spark, R and Tableau. Data scientists work with data according to business needs. They are responsible for data analysis. Big data developers design and implement programs that make the analysis possible. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more.

 

Job Placement Assistance Program

Our job placement program offers students one-on-one career counselling, and the chance to work with our corporate partners.

Candidates who fulfill the following criteria will be eligible for the program:

  • Scored 75% marks or above (resulting in a Platinum certificate) in the course
  • Successfully completed at least 2 quality projects
  • Scored 80% in all the mock technical interviews
  • Was never found plagiarizing code

 

Course Prep – To bring you up to speed

Phase One – The First Steps
Setting Up: Enroll for Course, Access Dashboard, Meet Mentor, Attend Orientation

Phase Two – The Training
Practice Drills: Live Sessions, Live Coding with Mentors, Case Studies & Assignments, Capstone Project

Phase Three – The Launchpad
Career Preparation: Resume Building, Reputation Management, Mock Interviews, Networking

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Course Outcomes:
  • Intensive 6-Month Program
  • Collaborative Assignments with Mentors
  • Master Statistics, Machine Learning, Deep Learning, and AI
  • Learn Tools like Python, TensorFlow, Spark, R, and Tableau
Course Details:

Target Audience

The course is suited for anyone with a zeal to learn about data science. It is ideal for aspiring data scientists from different backgrounds. Our students are generally analysts, developers, managers, information architects, researchers, and other working professionals looking to advance in the field of data science.

Access Timeframe

Lifelong Access to Course Material

Prerequisites

Prior knowledge of Python and statistics is useful. Nonetheless, these are covered in the course as well.
Certificate Info:

Type of Certification

International Certification

Format of Certification

Digital

Method of Obtaining Certification

You will get an internationally recognized certificate if you successfully complete the course.

Additional Details

Certificates are issued according to performances in assignments and projects.

Course Outline

Data scientists must know how to code - start by learning the fundamentals of two popular programming languages Python and R.
  • Basics of Python and R
  • Conditional and loops
  • String and list objects
  • Functions & OOPs concepts
  • Exception handling
  • Database programming
Once you have the core skill of programming covered– dip your feet in the nitty - gritties of working with data by learning how to wrangle and visualize them.
  • Reading CSV, JSON, XML and HTML files using Python
  • NumPy & pandas
  • Relational databases and data manipulation with SQL
  • Scipy libraries
  • Loading, cleaning, transforming, merging, and reshaping data
It is impossible to use data without knowledge of statistics. Collect, organize, analyze, interpret, and present data using these concepts of statistics.
  • Descriptive statistics & data distributions
  • Probability concepts and set theory
  • Probability mass functions
  • Probability distribution functions
  • Cumulative distribution functions
  • Modeling distributions
  • Inferential statistics
  • Estimation
  • Hypothesis testing
  • Implementation of statistical concepts in Python
Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.
  • Building models using below algorithms
  • Linear and logistics regression
  • Decision trees
  • Support vector machines (SVMs)
  • Random forests
  • XGBoost
  • K nearest neighbour & hierarchical clustering
  • Principal component analysis
  • Text analytics and time series forecasting
Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib and Tableau.
  • Interactive visualizations with Matplotlib
  • Data visualizations using Tableau
  • Tableau dashboard and story board
  • Tableau and R integration
Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.
  • Basics of neural network
  • Linear algebra
  • Implementation of neural network in Vanilla
  • Basics of TensorFlow
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs)
  • Generative models
  • Semi-supervised learning using GAN
  • Seq-to-seq model
  • Encoder and decoder
Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.
  • Introduction to Big Data & Spark
  • RDD's in Spark, data frames & Spark SQL
  • Spark streaming, MLib & GraphX
The course culminates in an enterprise project for a fictitious client that will expose you to every stage of the data science process – from data acquisitionand preparation to evaluation, interpretation, deployment, operations, and optimization. The project is an opportunity for you test your skills and demonstrate your ability to invent solutions for real world problems.

Technical Requirements

  • Microsoft® Windows® 7/8/10 (32- or 64-bit)
  • 4GB RAM minimum, 8 GB RAM (recommended)
  • i3 or higher processor
  • Internet speed: Minimum 1 Mb/s
  • Intel® VT-x (Virtualization Technology) enabled

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