Data Science Master's Program

Data Science is a highly in-demand field that has grown 45% in the last year alone. According to article on Hindu Business Line there are 97,000 job positions left vacant in India.

SILAN Technology is a leading training institute specializing in Data Science with Python certification course. Designed by industry experts, the course is aligned with the latest best practices to transform you into Data Scientiest / Data Analyst / Data Engineer.

You’ll learn Data Science with Python concepts in detail and implement many Machine Learning and deep learning algorithms, build artificial neural networks, and traverse layers of data abstraction to understand the power of data and prepare you for an exciting career in Data Science.

Syllabus:

• Introduction to Python
• What is Python?
• Why Python?
• Python Features.
• Understanding Python variables and Data Types.
• Python basic Operators.
• Understanding python blocks.
• Python Collections
•  List
•  Set
•  Tuple
•  Dictionary
• Python Flow Control
•  Selection Statements
•  If-else
•  If-elif-else
•  Iterative Statements
•  while loop
•  for loop
•  break
•  continue
•  range
•  pass
• Python Functions
•  Function call
•  Function definition
•  Recursion
•  Lambda function
•  Positional argument & keyword argument
•  Function Overloading.
•  Python String
•  Python Regular Expression
• Python OOPS
•  Python class and object
•  python constructor and destructor
•  Python Inheritence
•  Python Exception Handling
•  Python Multithreading
• Python for Data Science
•  Python pandas
•  Python numpy
•  Python matplotlib
•  Python seaborn
• 3-5 Capstone Projects on pandas and numpy
• Statistics
•  Introduction to Basic Terms
•  Variables
•  Random Variables
•  Population, Sample, Population Mean,
•  Sample Mean
•  Population Distribution, Sample Distribution and Sampling Distribution
•  Mean, Median ,Mode
•  Range
•  Measure Of Dispersion
•  Variance
•  Standard Deviation
•  Gaussian/Normal Distribution
•  Standard Normal Distribution
•  Z score
•  Probability Density Function
•  Cumulative distribution function
•  Hypothesis Testing
•  Many different plotting graphs
•  Kernel Density Estimation
•  Central Limit Theorem
•  Skewness of Data
•  Covariance
•  Pearson Correlation Coefficient
•  Spearman Rank Correlation Coefficient
•  Importance of Correlation
•  Hypothesis Testing
•  Null Hypothesis
•  Alternative Hypothesis
•  Understanding Hypothesis testing
•  (T-test,Chi square test, p values)