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Appreciation From Professionals
DataTrained Data Science Training Program has changed career of many individuals and has witnessed career transition to analytics/data science for some of them as well.
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.
Rakshit Jain
Data Scientist, Optum
I saw an ad from DataTrained on facebook and I contacted them straight away and inquired about their Data Science online course. Their counselor took me through the complete journey of what they offer and what is data science all about. After continuous conversation for a few weeks, I was pretty sure about the course and now I knew where I need to invest my money and hard work.
Surbhi Jain
Sr. Data Specialist Bank of America
I did my research before deciding which course I should register myself for and of all the courses that I have found, the one offered by DataTrained was completely dedicated to analytics, after enrolling for Postgraduate Program for Data Science, I realized DataTrained Data Science course was ideal for me.
Saurabh Chauhan
Data Analyst at KPMG India
All faculty members in DataTrained are well known and they are available round the clock to discuss any course related query.After completing my course I was so confident and cracked my first Interview with Amazon and I have completed a successful 1 year with them. Big thanks to DataTrained to help me in selecting a perfect job for me.
Rupam Kumar Chaurasia
Head Sales, Glenmark
The program is a well-balanced mix of pre-recorded classes, live sessions on weekends and printed reading materials they sent to my address. My mentor was Amit Kaushik and he helped me in getting that confidence and completing my assignments on time.I have almost completed the course and have been able to crack Glenmark interview.Thank you so much DataTrained.
Vanshika Rathi
Data Analyst Ola (ANI Technologies Pvt. Ltd)
Once I Joined DataTrained my learning curve started to grow steeply and as per the mentors I followed the new approach to get Data Science job.If I am successfully placed with one of the biggest data science firm complete credit lies with DATATRAINED and their competent Faculty.
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.
Why PGP-Data science stands-out from other programs?
| Attributes | Datatrained PGP-Data Science | Other Online Data Science Programs |
|---|---|---|
| Theory & Practical | Theory & Practical (20% & 80%) | Theory & Practical (80% & 20%) |
| Live Session | Live Session with Mentor | No live Session |
| Pre-recorded Videos | Pre-recorded videos are given as support | Pre-recorded Videos are preferred |
| Internship | 6 months Internship part of the core curriculum | No Internship is Provided |
| Remote Assistance | Remote Assistance is provided | No Remote assistance is provided |
| Projects | 40 real-life Projects | Only 5 to 10 Dummy Projects |
| Real-Time Data | Real-Time Data from tied-up companies are provided | No Real-Time data is shared |
| Live Projects | Live Projects goes till Complete Production Level | No Production Environment is introduced |
| Server Access | Virtual Private Server is available
(Internship Client will Provide access to their Server) |
No server access |
| Softwares | Anaconda, Core python with Linux, Pycharm is used | Only Anaconda is used |
Why PGP-Data science stands-out from other programs?
| Datatrained PGP-Data Science | Other Online Data Science Programs |
|---|---|
|
Theory & Practical (20% & 80%) |
Theory & Practical (80% & 20%) |
|
Live Sessions with Mentor |
No live Sessions |
|
Pre-recorded videos are given as support |
Pre-recorded Videos are preferred |
|
6 months internship part of the core curriculum |
No Internship is Provided |
|
Remote Assistance is provided |
No Remote assistance is provided |
|
40 Dummy Projects are Provided |
Only 5 to 10 Dummy Projects are Provided |
|
Real-Time Data from tied-up companies are provided |
No Real-Time data is shared |
|
Live Projects goes till Complete Production Level |
No Production Environment is introduced |
|
Virtual Private Server is available |
No server access |
|
Anaconda, Core python with Linux, Pycharm is used |
Only Anaconda is used |
Program Highlights
PGP in Data Science, Machine Learning & Neural Networks in collaboration with IBM
Online & Offline | 12 months
- Online Content + Live Mentoring Sessions.
- 400+ hours of overall learning.
- 40+ Projects & 6 Months Online Internship.
- Develop expertise in Python, MySQL ,Google Cloud & Linux.
- Eligibility: Minimum Graduate From any Recognised University.
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.
Foundations
- 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.
Our Hiring Partners
Some of the organizations that have been hiring talent from DataTrained
Learn Data Science from Top Experts & Faculties From Diffrent Industries
Mahdi Noorian
Data Scientist
IBM
Jay Rajasekharan
Data Scientist
IBM
About PGP-Data Science
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 12-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.