Best institute for DATA SCIENCE training IN  HYDERABAD

Kosmik Provides Data Science training in Hyderabad. We are providing lab facilities with complete real-time training. Training is based on complete advance concepts. So that you can get easily

Introduction to Data Science

      • Why it is important and who are eligible

 

Use Cases/Business Application (Retail, CPG, Banking, Telecom etc.)

      • Different scenario where DS can be applied to solve business problems

 

Basics of Statistics –

      • Descriptive Statistics for
      • Mean, Median, Mode, Quartile, Percentile, Inter-Quartile Range
      • Standard Deviation, Variance
      • Descriptive Statistics for two variables
      • Z-Score
      • Co-variance, Co-relation
      • Chi-squared Analysis
      • Hypothesis Testing

 

Probability concepts –

      • Basic Probability, Conditional Probability
      • Properties of Random Variables
      • Expectations, Variance
      • Entropy and cross-entropy
      • Estimating probability of Random variable
      • Understanding standard random processes

 

Data Distributions

      • Normal Distribution
      • Binomial Distribution
      • Multinomial Distribution
      • Bernoulli Distribution
      • Probability, Prior probability, Posterior probability
      • Naive Bayes Algorithm

 

Basic Mathematics for Data Science

      • Limits,
      • Derivatives, Partial Derivatives
      • Gradients, Significance of Gradients

 

Mastering Python/R Language

      • How to install python (Anaconda), sciKit Learn
      • How to work with Jupyter Notebook and Spyder IDE
      • Strings, Lists, Tuples, and Sets
      • Dictionaries, Control Flows, Functions
      • Formal/Positional/Keyword arguments
      • Predefined functions (range, len, enumerates etc…)
      • Data Frames
      • Packages required for data Science in R/Python

 

Introduction to NumPy

      • One-dimensional Array, Two-dimensional Array
      • Pre-defined functions (arrange, reshape, zeros, ones, empty)
      • Basic Matrix operations
      • Scalar addition, subtraction, multiplication, division
      • Matrix addition, subtraction, multiplication, division and transpose
      • Slicing, Indexing, Looping
      • Shape Manipulation, Stacking

 

Introduction to Pandas

      • Series, DataFrame, GroupBy
      • Crosstab, apply and map

 

Data Preparation Techniques

      • Applications of PCA: Dimensionality Reduction
      • Feature Engineering (FE)
      • Combine Features
      • Split Features

 

Data visualization

      • Bar Chart
      • Histogram
      • Box whisker plot
      • Line plot
      • Scatter Plot and Heat Maps

 

Machine Learning Algorithm – Data Preparation and Execution

      • Linear Regression
      • Logistic Regression
      • Optimization (Gradient Descent etc.)
      • Decision Tree
      • Random Forest
      • Boosting and AdaBoost
      • Clustering Algorithms (KNN and K-Means)
      • Support Vector Machines
      • Nave Bayes Algorithm
      • Neural Networks
      • Text Mining (NLTK)
      • Introduction to Deep learning

 

Note: All these Algorithms will be explained using one case study and executed in python.