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Data Science Training

August 1, 2021 - August 1, 2025

Free

Professional Data Science Training

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, structured or unstructured, similar to data mining.
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization

Curriculum
Our Courses prepared by Industry experts and Data Science Consultants
Lab Support
Our technical team is ready to assist you Online with Best practices
Scenarios
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam

Introduction to Data Science

  •  What is Data Science?
  •  Why now?
  •  Where Data Science is applicable?

Business Statistics

Introduction to statistics

Summarizing Data

      • Central Tendency measures – Mean, Median and Mode

      • Measures of Variability – Range, Interquartile Range, Standard Deviation and Variance

      • Measures of Shape – Skewness and Kurtosis

      • Covariance, Correlation Data Visualization

      • Histograms

      • Pie charts

      • Bar Graphs

      • Box Plot Probability basics

Parametric and Non parametric Statistical Tests

      • ‘f’ Test

      • ‘z’ Test

      • ‘t’ Test

      • Chi-Square test Probability Distributions

      • Expected value and variance

      • Discrete and Continuous

      • Bernoulli Distribution

      • Binomial Distribution

      • Poisson Distribution

      • Normal Distribution

      • Exponential Distribution

      • Empirical Rule

      •  Chebyshev’s Theorem

Sampling methods and Central Limit Theorem

      • Overview

      • Random sampling

      • Stratified sampling

      • Cluster sampling

      • Central Limit Theorem

Hypothesis Testing

      • Type I error

      • Type II error

      • Null and Alternate Hypothesis

      • Reject or Acceptance criterion

      • P-value 

Confidence Intervals

 ANOVA

    • Assumptions

    • One way

    • Two way

Artificial Intelligence – Machine Learning Introduction

Introduction to Machine Learning

  • What is Machine Learning?

  • Statistics (vs) Machine Learning

  • Types of Machine Learning

  Supervised Learning

  Un-Supervised Learning

  Reinforcement Learning


Artificial Intelligence – Supervised Machine Learning

Classification

  • Nearest Neighbor Methods (knn)
  • Logistic

Tree based Models – Decision Tree

  • Basics
  • Classification Trees
  • Regression Trees

 

Probabilistic methods

  • Bayes Rule
  • Naïve Bayes Regression Analysis
  • Simple Linear Regression
  • Assumptions
  • Model development and interpretation
  • Sum of Least Squares
  • Model validation
  • Multiple Linear Regression Regression Shrinkage Methods
  • Lasso
  • Ridge

Advanced Models – Black Box

  • Support Vector Machine
  • Neural Networks

 Ensemble Models

  • Bagging
  • Boosting
  • Random Forests 

Optimization

  • Gradient Descent (Batch and Stochastic) 

Recommendation Systems

  • Collaborative filtering
  • User based filtering

Item based filtering

Artificial Intelligence – Unsupervised Machine Learning

  • Association Rules (Market Basket Analysis)

  • Apriori Cluster Analysis

  • Hierarchical clustering

  • K-Means clustering Dimensionality Reduction

  • Principal Component Analysis

  • Discriminant Analysis (LDA/GDA)

 

Model Validation

Confusion Matrix ROC

Curve (AUC) Gain and 

Lift Chart

Kolmogorov-Smirnov Chart Root Mean 

Square Error (RMSE)Cross Validation

  • Leave one out cross validation (LOOCV)

  • K-fold cross validation

 

Artificial Intelligence – Natural Language Processing

  • Introduction to Natural Language Processing Sentiment

  •  Analysis

  • Text Similarity

Artificial Intelligence – Deep Learning

  • Deep Learning Introduction

  • Convolutional Neural Network 

  • Recurrent Neural Network

 

R Programming Language

Introduction

  • R Overview

  • Installation of R and RStudio software

  • Important R Packages

  • Datatypes in R – Vectors, Lists, Matrices, Arrays, Data FramesDecision making & Loops

  • If-else, while, for

  • Next, break. try-catch 

Functions

  • Writing functions

  • Nested functions

 Built-in functions

  • Vapply, Sapply, Tapply, Lapply etc.Data Preparation/Manipulation

  •  Reading and Writing Data

  • Summarize and structure of data

  • Exploring different datasets in R

  • Subsetting Data Frames

  • String manipulation in Data Frames

  • Handling Missing Values, Changing Data types, Data Binning Techniques,Dummy Variables Data Visualization using ggplot2

  • Basic charts – Histograms, Bar plots, Line graphs, Scatter plots etc.

Numpy Pandas

  • Introduction to Dataframes

  • Conversion of written R codes into pythonScipy-

     Machine Learning in Python

     Beautiful Soup 

     Matplotlib


 

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Spoorthy solutions curriculum is much easier and simple as they taught basics to understand the right approach used in data modelling, While training I was able to apply for opportunities and got placed
Learning for first Job
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Spoorthy solutions has helped me acquire the right understanding of Data science strategies, the lab was pretty crucial, recommend practicing everyday and make good use of support form spoorthy solutions
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Details

Start:
August 1
End:
August 1, 2025
Cost:
Free
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Spoorthy Software Solutions
Phone:
+91 040-40208208
Email:
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https://spoorthysolutions.com

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Hyderabad, Telangana 500090 India
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Phone:
+91 040-40208208
Website:
https://spoorthysolutions.com/

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