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Data Science

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Upcoming Batch Schedule for Online Training

Tecnosoft provides flexible timings to all our students. Here are the Online Training Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

  • 27-04-2020 Monday (Monday - Friday)Weekdays Regular 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionCourse Fees
  • 30-04-2020 Thursday (Monday - Friday)Weekdays Regular 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionCourse Fees
  • 25-04-2020Saturday (Saturday - Sunday)Weekend Regular11:00 AM (IST) (Class 3Hrs) / Per SessionCourse Fees
  • 25-04-2020Saturday (Saturday - Sunday)Weekend Fast-track 10:00 AM (IST)(Class 6Hrs - 7Hrs) / Per SessionCourse Fees
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ABout Course Description :

Data Science is the study of the generalize extraction of knowledge from data. Being a data Scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and programming languages along with a good understanding of the craft of problem formulation to engineer effective solutions.

This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data-product creation, evaluation, and effective communication.

The focus in the treatment of these topics will be a balanced approach on breadth and depth, and emphasis will be placed on integration and synthesis of concepts and their application to real time problems. To make the learning contextual, real data sets from a variety of disciplines will be used.

Program Highlights :

  • Most Comprehensive Curriculum
  • Trained by passionate and Industry experts
  • Each concept will be explained by golden rule
    • Theory – Example – Software Implementation (R/Python) – Real-Time applicability
  • Designed for the Industry
  • Live Project
  • Placement Assistance

Audience :

Any degree. No programming and Statistics knowledge required

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

Introduction to statistics

Summarizing Data

  • Central Tendency measures – Mean, Median and Mode
  • Measures of Variability – Range, Inter quartile Range, Standard Deviation and variance
  • Measures of Shape – Skew ness and Kurtosis
  • Co-variance, Correlation

Data Visualization

  • Histograms
  • Pie charts
  • Bar Graphs
  • Box Plot
  • ‘f’ Test
  • ‘z’ Test
  • ‘t’ Test
  • Chi-Square test
  • Expected value and variance
  • Discrete and Continuous
  • Bernoulli Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Exponential Distribution
  • Empirical Rule Chebyshev’s Theorem
  • Overview
  • Random sampling
  • Stratified sampling
  • Cluster sampling
  • Central Limit Theorem
  • Type I error
  • Type II error
  • Null and Alternate Hypothesis
  • Reject or Acceptance criterion
  • P-value
  • Assumptions
  • One way
  • Two way
  • What is Machine Learning?
  • Statistics (vs) Machine Learning
  • Types of Machine Learning
    1. Supervised Learning
    2. Un-Supervised Learning
    3. Reinforcement Learning


  • 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


  • Gradient Descent (Batch and Stochastic)

Recommendation Systems

  • Collaborative filtering
    – User based filtering
    – Item based filtering

Association Rules (Market Basket Analysis)

  • Apriori

Cluster Analysis

  • Hierarchical clustering
  • K-Means clustering

Dimensionality Reduction

  • Principal Component Analysis
  • Discriminant Analysis (LDA/GDA)
  • Confusion Matrix
  • ROC Curve (AUC)
  • Gain and Lift Chart
  • Kolmogorov-Smirnov Chart
  • Root Mean Square Error (RMSE)
  • Cross Validation
    1. Leave one out cross validation (LOOCV)
    2. K-fold cross validation
  • Introduction to Natural Language Processing
  • Sentiment Analysis
  • Text Similarity
  • R Programming Language


  • R Overview
  • Installation of R and RStudio software
  • Important R Packages
  • Datatypes in R – Vectors, Lists, Matrices, Arrays, Data Frames

Decision making & Loops

  • If-else, while,for
  • Next, break.try-catch


  • Writing function
  • Nested functions

Built-in functions

  • Vapply, Sapply, Tapply, Lapplyetc.

Data Preparation/Manipulation

  • Reading and Writing Data
  • Summarize and structure of data
  • Exploring different datasets in R
  • Sub Setting 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.


  • How is Python different from R
  • Installing Anaconda- Python
  • Setting up with spyder

Datatypes in Python

Importing modules

Introduction to Strings

String manipulation

  • For
  • While
  • If else
  • Lambda
  • apply


  • Introduction to Dataframes
  • Conversion of written R codes into python

Scipy-Machine Learning in Python

Beautiful Soup


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