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Data Science in Python Roles Based Training

MODULE 1: APPLIED INTRODUCTION TO PYTHON

  • Installation of Anaconda setup (Data Science Development Environment)
  • Python List , Tuple, Set, Dictionary operations
  • Python More on Strings
  • Python Dates and Times
  • Advanced Python Lambda and List Comprehensions
  • Accessing/Importing and Exporting data using Python modules
  • Csv, excel, Database import and export
  • Building and running Scheduler (Python Jobs) in Python
  • Advanced Python Demonstration: The Numerical Python Library (NumPy)
  • Advanced Python Demonstration: SciPy

MODULE 2: STATISTIC AND PROBABILITY CONCEPTS

  • Basic of Probability, Independent and Dependant events
  • Conditional Probability and Bayes Theorem
  • Distribution of Data – Normal, Binomial, Gaussian
  • Different types of Data :
  • Continuous , Categorical, Range
  • Mean, Median, Mode, Range
  • Determination of statistical techniques :
  • Standard Deviation, Variance, Covariance, Correlation
  • Testing of Hypothesis – which covers
  • Level of Significance (LOS), Level of Confidence, P-Value, T test,
  • Z-test, ANOVA Test, CHI -Square Test

MODULE 3: DATA EXPLORATION WITH PANDAS

  • The Series Data Structure
  • Querying a Series
  • The Data-Frame Data Structure
  • Data-Frame Indexing and Loading
  • Querying a Data-Frame
  • Indexing Data-frame
  • Understanding business problem
  • Selecting columns from Pandas Data Structures
  • Treating with missing values, outliers, NaN values
  • Creating new columns
  • Aggregate data ( use: groupby, merge, pivot, lambda)
  • Identifying unique values in data
  • Filter Data
  • Using basic functionality of Pandas API

MODULE 4: APPLIED PLOTTING, CHARTING & DATA REPRESENTATION IN PANDAS

  • Introduction to Matplotlib Library
  • Plotting data
  • Analysing data via plotting
  • Plotting various statistical measurements and distribution
  • Plotting scatter, bar, line charts

MODULE 5: SUPERVISED LEARNING AND MODEL BUILDING

  • Process of Machine Learning
  • Model Building based on Data sets
  • Splitting Data: Training and Test sets
  • Regression Analysis (Linear, Multiple, Logistics Regression)
  • Classification concepts and Distance Functions
  • K-nn Algorithm concept and demonstration with data sets
  • Bayes Classification concept and demonstration with data sets
  • Decision Tree Algorithm concept and demonstration with data sets
  • Random Forests - Ensembling Techniques and Algorithms

MODULE 6: UNPERVISED LEARNING AND MODEL BUILDING

  • Unsupervised Learning and Clustering Techniques
  • Centroid-based Clustering: K- Mean Algorithm concept and demonstration
  • Hierarchical Clustering concepts and Applications
  • Density-based Clustering: DBSCAN Algorithm concept and demonstration

MODULE7: DIMENSION REDUCTION TECHNIQUES

  • Dimension Reduction Introduction
  • Why Dimension Reduction Required
  • LDA (Linear Discriminant Analysis) concept and applications
  • PCA (Principle Component Analysis) concept and applications

MODULE8: TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

  • Introduction - Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and
  • Decomposition
  • Classification of Techniques(Pattern based - Pattern less)
  • Basic Techniques - Averages, Smoothening
  • Advanced Techniques - AR Models, ARIMA
  • MODULE9: DATA SCIENCE PROJECTS WITH DATA SETS

  • Applying different algorithms to solve the business problems and bench mark the results

MODULE10:

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  • Real Time Projects
  • Resume Preparation
  • Mock Interviews
  • Interview Questions & Answers
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Peopleclick

Expert in Data Science

  • +919164161200
  • info@people-click.com
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Peopleclick

Expert in Data Science

  • +919164161200
  • info@people-click.com