Data Science in Statistical approach (Level 1), Certificate based Saturday course.

A. Course title: Data Science in Statistical approach (Level 1)

Data science is a multi-disciplinary field that uses scientificmethods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

B. After learning the course : Trainee will be able to create data lake, to make data preprocessing, and to mine the data for discovery of new knowledge. Trainees will be able to improve the CV with the knowledge of advanced computing technology.

Objectives :To disseminate knowledge about (a) Creation of data lake, (b) Data cleaning and (c)Basic Data mining.

C. Time : September to December, 2019.

D. Organized by :Rabindrik Psychotherapy Research Institute, 3C /2, G.Mondol Road, Kolkata-700002. Opposite of the main gate of Rabindrabharati University, Kolkata. Course will be provided here.

E. Educational Qualifications : Graduation and Post graduation in Statistics, Information technology, Computer science, Management, Social Science, Bio science, Medical or Health sciences.

F. Eligibility : Good knowledge about basic statistics.

G. Course fee : Non refundable one time Rs 8k for students, Rs 9k for Professionals and Rs. 10k for sponsored candidates.

H. Certificate : Course completed certificate will be provided to successful candidates.

I. Important dates

1. Online form fill up starts: 21.8.19

2. Acceptance of admission : 30.8.19

3. Submission of Registration fees : 4.9.19

4. Class starts : 7. 9.19. 

J. Class timing : 4:30 to 6 PM (only on Saturday) 

I. Online Application : https://docs.google.com/forms/d/e/1FAIpQLSfAKHj6kKhqR8l6LMlapGldW1EIUM0_Aj9Gz-WuUFIdDGkDsg/viewform

J. Course outline :

Block 1:BIG data and Data reservoir (September)

1.1 Characteristics of BIG data, type, Scope and applications
1.2 Unorganized data and Organized data
1.3 Data retrieval algorithm
1.4 Data lake and Data Warehouse
1.5 Data modelling and architecture
1.6 Practical : Creation of Data architecture in statistics.

October 

Block 2
2. Data cleaning
2.1 Data scaling statistics
2.1 Data preprocessing
2.2.Data similarity and dissimilarity
2.3.Data exploration and Data visualization
2.4 Practical : Data visualization of processed and unprocessed data.

November - December 

Block 3
Data Mining
3.1 Aggregation
3.2 Sampling
3.3 Feature subset selection
3.4 Discretization and Binarization
3.5 Attribute transformation
3.6 Dimensionality reduction
3.6 Practical : Data attribution change and Data visualization.