Data Science with R Certification Course

Description

Data Science with R certification course makes you an expert in data analytics using the R programming language. This online training enables you to take your Data Science skills into a variety of companies, helping them analyze data and make more informed business decisions.

What's Included in Data Science with R Certification Course

 

a) Tailor-made and personalized learning program according to current industry
b) Live Instructor-Led & Self-Paced Learning options
c) Schedule training sessions according to your calendar
d) Direct project coaching and support
e) Comprehensive curriculum curated by industry advisors
f) Direct 24 hours service & support

 

 

Course Curriculum

 

Module1: Python Basics: It will help learn the tool, Python to be used for working with data

 

a) Introduction to Python

b) OOP: Object & Class

c) Serialization: Pickle Library

d) Variables

e) Lists

f) Tuples

g) Dictionary

h) Sets

i) List and Dictionary Comprehensions

j) Conditional Statements (If, If-else,elif)

k) Loops (For, While)

l) Functions

m) Lambda Function

n) Apply Function

 

Module2: Python NUMPY Library: It is used to perform a wide variety of mathematical operations on arrays

 

a) Array Characteristics

b) Array Creation (arrange, linspace, flatten)

c) Array Indexing (Slicing)

d) Array Manipulation

1) Reshape

2) Concatenate

3) Append

4) Insert

5) Delete

6) Transpose

 

Module3: Python PANDAS Library: It is used for data manipulation, data cleaning, data analysis

 

a) Series

b) Data Frames

c) Reading csv file

d) Sub Setting / Filtering / Slicing Data

e) Dropping rows & columns

f) Adding/Deleting columns

g) Binning 

h) Renaming columns or rows

i) Sorting 

j) Data type conversions

k) Handling duplicates /missing

l) Broadcasting

m) Group by Function

n) Map Function

o) Visualization (bar graph, histogram, box plot)

p) Merging (Inner, Left, Right, Outer)

q) EDA

 

Module4: Python MATPLOTLIB Library: Data Visualization part1 

 

a) Bar Plot

b) Stacked Bar Plot

c) Histogram

d) Line Chart

e) Box plot

f) Pie-Chart

 

Module5: Python SEABORN Library: Data Visualization part2 

 

a) Bar Plot

b) Histogram

c) Pairwise Plots: Joint Plot, Pair Plot

d) Categorical Scatter Plot: Strip-plot, Swarm-plot

e) Box-Plot

f) Violin Plot

g) Cat Plot

h) Facet Grid

i) Pair Grid

j) Line Plot

 

Module6: Basic Statistics: For business analysis

 

a) Type of Data

b) Statistics

c) Type of Statistics

d) Descriptive Statistics

e) Mean, Median, Mode (Measures of Central Tendency)

f) Standard Deviation, Variance (Measures of Dispersion)

g) Normal Distribution

h) Standard Normal Distribution

i) Standard Error

j) Sampling

k) Probability

 

Module7: Advance Statistics: For business analysis

 

a) Confidence Interval

b) T-Test & Z-Test

c) P-value

d) Hypothesis Testing

e) Type I Error & Type II Error

f) Chi-Square Test

g) ANOVA

h) Covariance

i) Correlation

 

Module8: Machine Learning

 

a) Supervised

b) Unsupervised

 

Module9: Supervised Machine Learning: Linear Regression (Solve business problems where we have to predict a value)

 

a) Introduction 

b) Assumptions (Linearity, Hetroskedasticity, Multivariate Normality, etc)

c) Data Preparation (Outlier Treatment, Missing Value Imputation)

d) Building Linear Regression Model

e) Understanding model metrics (p-value, R-square/Adjusted R-square etc)

f) Multicolinearity (VIF)

g) Model Validation (MAPE,RMSE)

h) Case study

 

Module10: Supervised Machine Learning: Logistic Regression (Used for binary classification business problems)

 

a) Introduction

b) Linear Regression Vs. Logistic Regression

c) Data Preparation (Outlier Treatment, Missing Value Imputation, Dummy Variable Creation)

d) Building Logistic Regression Model

e) Understanding model metrics (p-value)

f) Multicolinearity (VIF)

g) Model Validation (Confusion Matrix, ROC curve, AUC, etc)

h) Case study

 

Module11: Supervised Machine Learning: Decision Tress (Used for multi-class classification business problems & regression business problems)

 

a) Introduction 

b) Types 

c) Entropy, Gini Index, Chi-Square

d) Overfitting

e) Pruning

f) Cross – Validation

g) Case study

 

Module12: Supervised Machine Learning: Ensemble (Used for multi-class classification business problems & regression business problems)

 

a) Introduction

b) Bagging

1) Random forest

c) Boosting

1) Gradient Boosting Machines (GBM)

d) Case study

 

Module13: Supervised Machine Learning: KNN (Used for multi-class classification business problems & regression business problems)

 

a) Introduction

b) Working of KNN

c) Optimal value of K

d) Case study

 

Module14: Unsupervised Machine Learning: Clustering (Used for segmenting data points into different groups)

 

a) Introduction 

b) K -Means Clustering 

c) Cluster Evaluation and Profiling

d) Case study

 

Module15: Unsupervised Machine Learning: PCA (Used for segmenting data points into different groups)

 

a) Introduction 

b) Curse of dimensionality

c) Process of working

d) Case study

 

Module16: Unsupervised Machine Learning: Isolation Forest (Used for anomaly detection business problems)

 

a) Introduction 

b) Contamination Factor

c) Case study

 

Module17: Time Series Forecasting: Used for inventory planning or forecasting business problems

 

a) Introduction

b) Time Series Components : Trend, Seasonality, Cyclicity

c) Smoothening Techniques– Moving Averages, Exponential

d) ARIMA

e) Accuracy

f) Case study 

 

Module18: Text Analytics: Used for text mining business problems working with unstructured data

 

a) Introduction

b) Text Pre-processing

1) Noise Removal

2) Lemmatization

3) Stemming

4) Feature Engineering on Text Data

5) Bag of words

6) TF-IDF

c) Case study

 

Module19: AI: Deep Learning, Keras

 

a) Introduction: Deep Learning

b) Deep Learning vs Machine learning

c) Neural Networks

d) Activation Functions, hidden layers, hidden units

e) Backpropagation

f) Vanishing Gradient Problem

g) Exploding Gradient Problem

h) Perceptron & Multi-layer Perceptron

i) Case study

 

Module20: Model Deployment: Using model for predicting output on new input values

 

a) Flask

b) Case study