Data Science Training in Marathahalli Bangalore → Importance of Data Science

INTRODUCTION TO DATA SCIENCE 4
- Lecture1.1
  
  What is data Science? – Introduction 30 min
- Lecture1.2
  
  Importance of Data Science 30 min
- Lecture1.3
  
  Demand for Data Science Professional 30 min
- Lecture1.4
  
  Lifecycle of data science 30 min
- Lecture1.5
  
  Tools and Technologies used in data Science 30 min
- Lecture1.6
  
  Business Intelligence vs Data Science vs Data Engineer 30 min
- Lecture1.7
  
  Role of a data scientist 30 min
Fundamentals of Math and Probability 6

INTRODUCTION TO STATISTICS
- Lecture2.1
  
  Basic understanding of linear algebra, Matrices, vectors 30 min
- Lecture2.2
  
  Addition and Multiplication of matrices 30 min
- Lecture2.3
  
  Fundamentals of Probability 30 min
- Lecture2.4
  
  Probability distributed function and cumulative distributed function 30 min
- Lecture2.5
  
  Conditional Probability 30 min
- Lecture2.6
  
  Class Hand-on – Problem solving using R for vector manipulation Problem solving for probability assignments 30 min
Descriptive Statistics 4
- Lecture3.1
  
  Describe or summaries a set of data Measure of central tendency and measure of dispersion. 30 min
- Lecture3.2
  
  The mean, median, mode, Standard deviation, Variance, Range, kurtosis and skewness. 30 min
- Lecture3.3
  
  Histograms, Bar chart, Box plot 30 min
- Lecture3.4
  
  Class Hands-on- 5 Point summary Box Plot, Histogram and Bar Chart Exploratory analytics R Methods 30 min
Inferential Statistics 17
- Lecture4.1
  
  What is inferential statistics Different types of Sampling techniques Central Limit Theorem 30 min
- Lecture4.2
  
  Univariate & Bivariate Analysis 30 min
- Lecture4.3
  
  Correlations 30 min
- Lecture4.4
  
  Least Square Regression 30 min
- Lecture4.5
  
  Normal Distribution 30 min
- Lecture4.6
  
  Binomial Distribution & Quincunx 30 min
- Lecture4.7
  
  Point estimate and Interval estimate 30 min
- Lecture4.8
  
  Creating confidence interval for population parameter Characteristics of Z- distribution and T-Distribution Basics of Hypothesis Testing 30 min
- Lecture4.9
  
  Bias & Variance trade-offs 30 min
- Lecture4.10
  
  Type of test and rejection region 30 min
- Lecture4.11
  
  Type of errors in Hypothesis resting, Type-l error and Type-ll errors 30 min
- Lecture4.12
  
  False Positive & False Negative 30 min
- Lecture4.13
  
  P-Value and Z-Score Method 30 min
- Lecture4.14
  
  T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA) 30 min
- Lecture4.15
  
  Regression analysis in ANOVA 30 min
- Lecture4.16
  
  Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test 30 min
- Lecture4.17
  
  Case study and model run for ANOVA, ANCOVA 30 min
Hypothesis Testing 6
- Lecture5.1
  
  Hypothesis Testing Basics 30 min
- Lecture5.2
  
  Null Hypothesis 30 min
- Lecture5.3
  
  Alternate Hypothesis 30 min
- Lecture5.4
  
  p-Value 30 min
- Lecture5.5
  
  Type of test and Rejection Region 30 min
- Lecture5.6
  
  Type o errors-Type 1 Errors, Type 2 Errors P value method, Z score Method 30 min
Introduction to Machine Learning 6

UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING
- Lecture6.1
  
  What is Machine Learning? 30 min
- Lecture6.2
  
  Introduction to Supervised Learning, Unsupervised Learning & Semi-supervised Learning 30 min
- Lecture6.3
  
  What is Reinforcement Learning? 30 min
- Lecture6.4
  
  Variable Identification 30 min
- Lecture6.5
  
  Missing values treatment 30 min
- Lecture6.6
  
  Outliers treatment 30 min
Linear Regression 6
- Lecture7.1
  
  Introduction to Linear Regression Linear Regression with Multiple Variables 30 min
- Lecture7.2
  
  Disadvantage of Linear Models Interpretation of Model Outputs Understanding 30 min
- Lecture7.3
  
  Multi-colinearity 30 min
- Lecture7.4
  
  Missing & Outlier treatment 30 min
- Lecture7.5
  
  Understanding Heteroscedasticity 30 min
- Lecture7.6
  
  Case Study – Application of Linear Regression for CTG data 30 min
Logistic Regression 7
- Lecture8.1
  
  Introduction to Logistic Regression 30 min
- Lecture8.2
  
  Binary Logistic Regression 30 min
- Lecture8.3
  
  Multinomial Logistic Regression 30 min
- Lecture8.4
  
  Introduce the notion of classification Cost function for logistic regressio 30 min
- Lecture8.5
  
  Application of logistic regression to multi-class classification. 30 min
- Lecture8.6
  
  Confusion Matrix, Odd's Ratio and ROC Curve Advantages and Disadvantages of Logistic Regression 30 min
- Lecture8.7
  
  AIC & BIC 30 min
Decision Trees 10
- Lecture9.1
  
  Decision Tree – C4.5, CART, CHAID 30 min
- Lecture9.2
  
  How to build decision tree? Understanding CART Model Classification Rules 30 min
- Lecture9.3
  
  Overfitting Problem Stopping Criteria And Pruning 30 min
- Lecture9.4
  
  Underfitting 30 min
- Lecture9.5
  
  Gini Index 30 min
- Lecture9.6
  
  Informations Gain 30 min
- Lecture9.7
  
  How to find final size of Trees? Model A decision Tree. 30 min
- Lecture9.8
  
  MDS 30 min
- Lecture9.9
  
  Random Forests and Support Vector Machines Interpretation of Model Outputs 30 min
- Lecture9.10
  
  Case Study – 1 Business Case Study for Kart Model 30 min
Unsupervised Learning 10
- Lecture10.1
  
  Feature Selection & Feature Extraction 30 min
- Lecture10.2
  
  Feature Construction 30 min
- Lecture10.3
  
  Hierarchical Clustering 30 min
- Lecture10.4
  
  K-Means algorithm for clustering – groupings of unlabeled data points. 30 min
- Lecture10.5
  
  Principal Component Analysis(PCA) 30 min
- Lecture10.6
  
  Anomaly Detection 30 min
- Lecture10.7
  
  Association rules 30 min
- Lecture10.8
  
  Market Basket Analysis 30 min
- Lecture10.9
  
  Customer Segmentation 30 min
- Lecture10.10
  
  Dimensionality reduction on CTG 30 min
R Basics, background 4

PART C – R PROGRAMMING
- Lecture11.1
  
  Comprehensive R Archive Network 30 min
- Lecture11.2
  
  Demo of Installing R On windows from CRAN Website 30 min
- Lecture11.3
  
  Installing R Studios on Windows OS Setting Up R Workspace. 30 min
- Lecture11.4
  
  Getting Help for R-How to use help system Installing Packages – Loading And Unloading Packages 30 min
Getting familiar with basics 3
- Lecture12.1
  
  Operators in R – Arithmetic, Relational, Logical and Assignment Operators 30 min
- Lecture12.2
  
  Variables, Types Of Variables, Using variables Conditional statements, if else(),switch 30 min
- Lecture12.3
  
  Loops: For Loops, While Loops, Using Break statement, Switch 30 min
The R Programming Language- Data Types 2
- Lecture13.1
  
  Creating data objects from the keyword. How to make different type of data objects. Types of data structures in R 30 min
- Lecture13.2
  
  Arrays And Lists- Create Access the elements Vectors – Create Vectors, Vectorized Operations, Power of Vectorized Operations Matrices- Building the first matrices, Matrix Operations, Sub setting, visualizing subset Data Frames- create and filter data frames, Building And Merging data frames. 30 min
Functions And Importing data into R 4
- Lecture14.1
  
  Function Overview – Naming Guidelines Arguments Matching, Function with Multiple Arguments 30 min
- Lecture14.2
  
  Additional Arguments using Ellipsis, Lazy Evaluation Multiple Return Values 30 min
- Lecture14.3
  
  Function as Objects, Anonymous Functions Importing and exporting Data into R- importing from files like excel, csv and Minitab. 30 min
- Lecture14.4
  
  Import from URL and excel Files Import from database. 30 min
Data Descriptive Statistics, Tabulation, Distribution 1
- Lecture15.1
  
  Summary Statistics for Matrix Objects. Apply () Command. Converting an Object into a Table Histograms, Stem and Leaf Plot, Density Function. Normal Distribution 30 min
Graphics in R – Types of graphics 2
- Lecture16.1
  
  Bar Chart, Pie Chart, Histograms- Create and edit. Box Plots- Basics of Box plots- Create and Edit Visualization in R using ggplot2. 30 min
- Lecture16.2
  
  More about Graphs: Adding Legends to Graphs, Adding Text to Graphs, Orienting the Axis Label 30 min
R – Machine Learning Libraries/ Packages 7
- Lecture17.1
  
  Caret 30 min
- Lecture17.2
  
  Missing Values – Hmisc, MICE, rForest 30 min
- Lecture17.3
  
  ggplot2 30 min
- Lecture17.4
  
  Regressions – lm, glm 30 min
- Lecture17.5
  
  Tree based algorithms – rpart, party 30 min
- Lecture17.6
  
  Tree based algorithms – rpart, party 30 min
- Lecture17.7
  
  MASS & psych 30 min
PYTHON PANDAS 23
- Lecture18.1
  
  Introduction 30 min
- Lecture18.2
  
  Installing python pandas 30 min
- Lecture18.3
  
  Python Basics: Expressions, Data Types & Variables 30 min
- Lecture18.4
  
  Functions 30 min
- Lecture18.5
  
  Working with Lists 30 min
- Lecture18.6
  
  Working numPy arrays 30 min
- Lecture18.7
  
  Working numPy arrays 30 min
- Lecture18.8
  
  Reading and Writing Files 30 min
- Lecture18.9
  
  Working with CSV and JSON Data 30 min
- Lecture18.10
  
  Plotting with Matplotlib 30 min
- Lecture18.11
  
  Scatter plot, Line plot, Bar charts, Box plot, Plotting with Pandas 30 min
- Lecture18.12
  
  Working with Data Frames 30 min
- Lecture18.13
  
  Aggregate functions using Pandas 30 min
- Lecture18.14
  
  Data Processing, cleaning using Pandas 30 min
- Lecture18.15
  
  Working with numPy , sciPy Libraries 30 min
- Lecture18.16
  
  Dictionaries and Structuring Data 30 min
- Lecture18.17
  
  Manipulating Strings 30 min
- Lecture18.18
  
  Pattern Matching with Regular Expressions 30 min
- Lecture18.19
  
  Debugging 30 min
- Lecture18.20
  
  Frequency distributions 30 min
- Lecture18.21
  
  Missing Values 30 min
- Lecture18.22
  
  Outliers 30 min
- Lecture18.23
  
  Feature Engineering 30 min
Python ML Libraries – Introduction 3
- Lecture19.1
  
  Pandas 30 min
- Lecture19.2
  
  sci-kit learn 30 min
- Lecture19.3
  
  Seaborn, bokeh, plotly 30 min
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64 Comments

sudarshan

February 8, 2019

If you want to Master in data science course. The right approach is to understand the statistics and Machine learning concepts strongly. This is the forst point my trainer told to me. I have followed his instructions and today i am confident and feel i have very good knowledge on AI. Eminent IT has given me all opportunities to grow as a Data Scientist. Best trainings on AI and Data Science courses in Marathahalli, Bangalore.

INTRODUCTION TO DATA SCIENCE 4

Fundamentals of Math and Probability 6

Descriptive Statistics 4

Inferential Statistics 17

Hypothesis Testing 6

Introduction to Machine Learning 6

Linear Regression 6

Logistic Regression 7

Decision Trees 10

Unsupervised Learning 10

R Basics, background 4

Getting familiar with basics 3

The R Programming Language- Data Types 2

Functions And Importing data into R 4

Data Descriptive Statistics, Tabulation, Distribution 1

Graphics in R – Types of graphics 2

R – Machine Learning Libraries/ Packages 7

PYTHON PANDAS 23

Python ML Libraries – Introduction 3

Real Time Project 0

Resume Preparation Tips 0

Interview Guidance and Support 0

Job Support and Placement Assistance 0

Importance of Data Science

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