Get Started Towards Your Career Growth

Application Deadline: 15th October 2021
Ranked No 1 by: analytics jobs

About the Course

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DataTrained Post Graduate Program in Data Science is offered in collaboration with IBM. PGP-DS is mainly focused on empowering professionals & Students for fast-track career in Data Science & Artificial Intelligence. It is a 10-month complete program offered online & Offline with live interactive mentoring sessions on weekends.

This course enables learning through a combination of expert Mentoring Sessions, hands-on training and demos. You will get to work on 40+ hands-on labs and projects along with a real-time Industrial project with our Partners based on a real-world situation. You will become proficient in working with various environments including Anaconda, Linux , Google Cloud, Python & more. Freshers Looking for their career in Data Science will gain a solid foundation while those with some IT experience will gain a more structured and hands-on understanding of Data Science & Artificial Intelligence Technologies.

Why Join PGP - Data Science, ML & Neural Networks?

INTERNSHIP

6 Months internship ensures you graduate as an experienced data science professional rather than a fresher. You can go for an online internship along with your current job

RESUME FEEDBACK

Partnered with Analytics Jobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role

INTERVIEW PREPARATION

Regular mock HR and Technical interviews by mentors with personal guidance and support. The industry mentor helps students to take projects on Kaggle and move on to the status bar so that their resume looks competitive to the recruiters

PLACEMENTS

We generate the Ability Score of every individual which is then sent to our more than 250 recruitment partner organizations. At last, we organize campus placements every three months in Noida, Gurgaon, Ahmedabad, Bangalore, and Chennai to place our students

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What Our Students have to say?

Aaruni Khare
Data Scientist

DataTrained has helped me with the vital knowledge and skills that are needed for a data scientist role. The trainer starts with an example to make us comprehend the concept and then help us build the algorithms with the real industry datasets.

Ashish Kumar
Business Analyst at Bank of America

After going through many institutes courses, curriculum, classes time and fees, I founded that DataTrained suites to my needs as their classes were on weekends. So I do not have to leave my current job until I complete it and get a new job.I am now happy that I took the decision to move to Analytics. Thank you so much DataTrained.

Nikhil Bhargav
Data Scientist, ICICI Lombard

DataTrained helped me to move from a sales and marketing domain to a data science domain.

Krishna Vedamurthy
Sr. Data Analyst, DataTrained

I have completed my course with flying colors and now I am placed with DataTrained which I can personally rate as one of the best companies to work for. I hope my success story will help you in finding the best options for you all.

Course Curriculum

Freshers will gain a solid foundation while those with some experience will gain a more structured and hands-on understanding of Data Science technologies.

  • Introduction to Data Science
  • Data Science Era
  • Data Science involvment in Industries
  • Business Intelligence Vs Data Science
  • Data Science Life Cycle
  • Tools of Data Science
  • Introduction to Machine Learning
  • Basic Linux
  • Basic Operation in Python Variable , Assignment (VM’s and Containers)
  • Finctions : in-built Functions & Uder-defined Functions
  • Conditions: if, if-else, nested if-else, else-if
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Introduction to Data Structure
  • List: Different Data Types in a List, List in a List
  • Operations on List: Slicing , Splicing, Sub-setting
  • Conditions (True /False) on a List
  • Applying Functions on a List
  • Dictionary: Index , Value
  • Operations on Dictionary: Slicing , Splicing, Sub-setting
  • Conditions (True /False) on a Dictionary
  • Applying Functions on Dictionary
  • Modules & Packages
  • Regex Operations
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Introduction to SQL(Structure Query Language )
  • Basic SQL Statements
  • Advance SQL(Searching,Sorting,Grouping)
  • Access Database Using Python
  • Datatypes in an Array , Dimesnions of an Array
  • Operations on Array: Indexing, Slicing, Splicing, Sub-setting
  • Conditional (T/F) on an Array
  • Loops: For , While
  • Shorthand for For
  • Control Statements
  • Shape Manipulation
  • Linear Algebra
  • Python Pandas - Home
  • Python Pandas - Introduction
  • Python Pandas - Environmental Setup
  • Introduction to Data Structure
  • Python Pandas - Series
  • Python Pandas - DataFrame
  • Python Pandas - Panel
  • Python Pandas - Basic Functionality
  • Funtion Application
  • Python Pandas - Reindexing
  • Python Pandas - Iteration
  • Python Pandas - Sorting
  • Working With Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Python Pandas- Missing Data
  • Python Pandas- GroupBy
  • Python Pandas- Merging/Joining
  • Python Pandas- Concatenation
  • Python Pandas- Date Functionality
  • Python Pandas- Categorical Data
  • Python Pandas- Visualization
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Introduction To Statistics
  • Statistical Inference
  • Terminologies of Statistics Descriptive Statistics
  • Statistical Functions Measure of Centre
  • Mean
  • Median
  • Mode Measure of Spread
  • Variance Standard Deviation
  • Histogram Probablity
  • Normal Distribution
  • Binary Distribution
  • Poisson Distribution
  • Skewness
  • Bell Curve
  • Hypothesis Building & Testing
  • Chi-Square Test
  • Correlation Matrix
  • Scientific Computing with Python
  • Scipy and Its Characterstics
  • Scipy Sub-packages
  • Scipy Sub-packages - Integration
  • Scipy Sub-packages - Optimize
  • Linear Algebra
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data, Raw data & Processed Data
  • Data Wrangling , Exploratory Data Analysis, Visualization, MatplotLib
  • Bar Plot
  • Histogram Plot
  • Box Plot
  • Area Plot
  • Scatter Plot
  • Pie Plot
  • Seaborn
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Introduction to Machine Learning
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Data Preprocessing
  • Data Preparation
  • Introduction to Scikit Learn
  • Regression
  • Types, Algorithims
  • Linear Regression
  • Root Mean Squared Error
  • R2 Score
  • Logistic Regression
  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling Dimensional Model
  • Implementaion with Case Studies,Intro to Kaggle & UCI Repository
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Classification K-Nearest Neighbors
  • Metrics
  • Confusion Matrix
  • Classification Report
  • Support Vetor Machine
  • Kernel
  • Working with SVM
  • Naive Bayes
  • Hyper Parameter Optimization
  • Decision Tree Classfier
  • Random Forest Classifier
  • Ensemble Techniques & SVM Tuning
  • AUC_ROC Curve, Cross Validation
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Unsupervised Learning
  • Clustering Algorithms
  • K-means Clustering
  • Implementaion with Case Studies
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Recommendation Engine
  • Colaborative Filtering
  • This Course will cover 12+ Industrial Real-Time Case Studies
  • Specialization Course : Students can opt any one elective(s)
  • Elective 1: Natural language Processsing (NLP)
  • Elective 2: COmputer Vision
  • Pre-Reads (Attachment for Students )
  • Assignment ( For Students )
  • Assignment Solutions
  • Extra Boot Camp (Optionals based on Time):- Orientation , Resume Sessions Tableau.

Learn Data Science from Top Experts & Faculties From Different Industries

Dr. Deepika Sharma
Training Head - DataTrained
Mahdi Noorian
Data Scientist - IBM
Jay Rajasekharan
Data Scientist - IBM

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