Data science

1. Career
In
Data Science

2. WHY WE ARE TALKING ABOUT DATA
SCIENCE
1 ZB = 1 billion TBs
1 ZB = 1 Billion
TBs

3. WHAT IS DATA SCIENCE?
A multidisciplinary field that
explores structured and
unstructured data for
information and insights using
scientific procedures, processes,
algorithms, and systems.

4. WHO USES DATA SCIENCE?
• Facebooks
• Amazon
• Google
• Linkedln
• Netflix
• Microsoft
• YouTube

5. what Data Scientists Do?
•Processing large data sets
•Exploratory Data Analysis
•Data Cleaning
•Handling Missing Values
•Data Visualization
•Getting Insights from data to solve
business problem
•Building Machine learning Model
•Building Deep Learning Model
•Deploying Model
•Maintaining Model

6. Data Science Skills
• Programming Language (Python or R)
• Statistic (Descriptive and Inferential )
• Machine Learning
• SQL or Mogo DB

7. Learning Data Science with Python - Libraries

8. APPLICATIONS OF DATA SCIENCE-
RECOMMENDER SYSTEM

9. Used Car Price

10. APPLICATIONS OF DATA SCIENCE-IMAGE
PROCESSING

11. Fraud and Risk Detection

12. APPLICATIONS OF DATA SCIENCE-CLINICAL RESEARCH

13. Delivery Logistics

14. ABOUT COURSE
•Course Duration :- 4 Months
•Time :- Monday to Friday (8.30 pm to 10 pm)
•Group Discussions : - Saturday & Sunday
•Posts You can Apply to :-
1.Data Scientist
2.Data Analyst
3.ML Engineer

15. Syllabus
Module 1: Python
•Environment set-up
•Jupyter overview
•Python course
•Python Numpy
•Python Pandas
•Python Matplotlib
Python Seaborn
Module 2: Statistics
•Important statistical concepts used in data science
•Difference between population and sample
•Types of variables
•Measures of central tendency
•Measures of variability
•Skewness and Kurtosis
Module 3: Inferential statistics
•Normal distribution
•Test hypotheses
•Central limit theorem
•Confidence interval
•T-test
•Type I and II errors
•Student’s T distribution
Module 4: Regression and Anova
•Regression
•ANOVA
•R square
•Correlation and causation

16. Syllabus
Module 5: Exploratory data analysis
•In this lesson you will learn –
•Data visualization
•Missing value analysis
•The correlation matrix
•Outlier detection analysis
Module 6: Supervised and unsupervised ML
•Python Scikit tool
•Logistic and linear regression
•Decision tree classifier
•Neural networks
•Support vector machine
•Clustering
•Random Forest
•Natural Language Processing
•Market Basket analysis
•Recommender system
•Forecasting
•Deep learning
Module 7: SQL
•SQL Syntax
•SQL Data Types
•SQL Operators
•SQL Expressions
•SQL Windows Funct

17. Thank You

18. Questions ?