Data Science Bundle

Name: Data Science Course Bundle
Rating: 5 (695 reviews)

Call us directly at , : +91-7095447721, +91-9533344772 for paying the fees, seasonal discounts or any other information.

What you will get when you take this course:

World-class course content taught live by the expert Dr R Nageswara Rao.
Zoom link for the class daily from course start to end date to your mail.
Opportunity to clarify your doubts at the end of each class.
Placement Assistance by posting available jobs, exclusively for Datatechs students.
Certificate of Completion upon successful completion of the course.

Data Science bundle consists of 7 Modules:

PYTHON FOR DATA SCIENCE
STATISTICAL METHODS
TABLEAU
MACHINE LEARNING IN AI
DEEP LEARNING IN AI
NATURAL LANGUAGE PROCESSING (NLP) IN AI
COMPUTER VISION IN AI

Data Science Syllabus

MODULE 1: PYTHON FOR DATA SCIENCE

1.1. FUNDAMENTALS OF PYTHON

Advantages of Python
Python compiler and PVM
Python instillation and environment

1.2. DATATYPES IN PYTHON

strings
char
lists
tuples
range
sets
dictionaries

1.3. OPERATORS IN PYTHON

1.4. INPUT/OUTPUT

1.5. CONTROL STATEMENTS

if statement
if…else statement
if…elif…else statement
while loop
for loop
break statement
continue statement
pass statement

1.6. NUMPY ARRAYS

Array creation
Array attributes
1D and 2D Arrays
Matrix

1.7. FUNCTIONS IN PYTHON

Built in and User defined functions
Writing your own functions
Importing functions

1.8. MODULES AND PACKAGES

Modules
Packages
Imports

1.9. DATA ANALYSIS IN PYTHON USING PANDAS

Series
Dataframes
Creation of dataframes from different sources
Viewing data in dataframe
Operations on dataframe
Handling missing data

1.10.. DATA VISUALIZATION USING MATPLOTLIB

Line plot
Bar graph
Pie chart
Subplots
Histogram

1.11. DATA VISUALIZATION USING SEABORN

Distribution plot
Kde plot
Count plot
Box plot
Scatter plot
Sub plots
Lmplot
Pair plots

MODULE 2: STATISTICAL METHODS

2.1. INTRODUCTION TO STATISTICS

What is statistics?
Types of statistics
Descriptive statistics
Inferential statistics

2.2. STATISTICAL TERMS

Population
Sample
Variable (discrete and continuous)
Data and types of data
Qualitative (nominal and ordinal)
Quantitative (interval scale and ratio scale)

2.3. MEASURES OF CENTRAL TENDENCY

Mean
Median
Mode

2.4. PROBABILITY

Probability with replacement
Probability without replacement
Probability Mass Function (PMF)
Probability Density Function (PDF)

2.5. MEASURES OF SHAPE

Skewness
Kurtosis

2.6. MEASURES OF DISPERSION OR VARIABILITY

variance
std
percentile
quartile
range
IQR

2.7. APPLICATION OF VARIANCE OR STD

Empirical Rule
Problems on Empirical Rule
Chebyshev’s Theorem

2.8. PROBABILITY DISTRIBUTIONS

Normal distritution
Standard normal distribution
Sampling distribution of sample means
Central limit theorem
T- Distribution
Student T- Test
Chi Square Test (Goodness of Fit)
Binomial distribution
Bernoulli distribution
Geometric distribution
Hypergeometric distribution
Poisson distribution

2.9. HYPOTHESIS TESTING

Upper tail test
Lower tag test
Two tag test

2.10. ANOVA

1-way ANOVA
2-way ANOVA

MODULE 3: TABLEAU

3.1. INTRODUCTION TO TABLEAU

Tableau tools
Datatmes in Tableau
Viewing data

3.2. CREATING PIVOT TABLE

3.3. DATA BLENDING

3.4. CROSS DATABASE JOINS

3.5. CALCULATIONS ON DATA

Aggregate functions

3.6. DATA VISUALIZATION IN TABLEAU

Symbol maps
Bar chart
Stacked bar chin
Line chart
Pareto chart
Heat map
Pie chart
Scatter plot
Area chart
Dual Axis chart
Histogram
Bubble chart

3.7. DASHBOARD CREATION

MODULE 4: MACHINE LEARNING IN AI

4.1. EXPLORATORY DATA ANALYSIS (EDA)

4.2. OUTLIERS AND THEIR TREATMENT

4.3. SUPERVISED LEARNING VS UNSUPERVISED LEARNING

4.4. FEATURE EXTRACTION AND CONVERSION

One hot encoding using dummy variables
One hot encoding using One hot encoder

4.5. REGRESSION MODELS

Simple Linear regression
Multiple Linear regression
Polynomial Linear regression
Ridge regression
Bias and Variance tradeoff
Lasso regression
Elasticnet regression

4.6. CLASSIFICATION MODELS

Logistic regression
Naive Bayes (Gaussian NB and Multinomial NB)
KNN Classifier
SVM
Regularization
Kernel Trick
Decision Tree
Entropy
Gini Index
Random Forest
Conusion Matrix
Bootstrapping, Bagging and Boosting

4.7. UNSUPERVISED LEARNING

K-Means Clustering
Elbow technique

4.8. ASSOCIATION RULE LEARNING

Apriori Algorithm

4.9. MODEL SELECTION

Selecting appropriate model for our data

MODULE 5: DEEP LEARNING IN AI

5.1. INTRODUCTION TO DEEP LEARNING

Biological Neural Network
Artificial Neural Network
Perceptrons
Layers of a Network

5.2. ACTIVATION FUNCTIONS

Identity Function
Binary step function or Threshold function
Logistic function or Sigmoid function
ReLU function
Hyperbolic Tangent function
Softmax function

5.3. CREATING NEURAL NETWORK IN PYTHON

ANN
ANN with Activation functions

5.4. TENSORFLOW AND KERAS

Variables
Constants
Placeholders
Graph / Tensor / Session

5.5. ANN IN TENSORFLOW AND KERAS

5.6. CONVOLUTIONAL NEURAL NETWORK (CNN)

5.7. RECURRENT NEURAL NETWORK (RNN)

MODULE 6: NATURAL LANGUAGE PROCESSING IN AI (NLP)

6.1. NLP CONCEPTS

Tokenization
Stemming
Lemmatization
Stop words
POS

6.2. FEATURE EXTRACTION

CountVectorizer
Tf-idf Vectorizer

6.3. TEXT CLASSIFICATION USING NLP

MODULE 7: COMPUTER VISION IN AI

7.1. OBJECT DETECTION BY COMPUTER

1st Floor, Above Midland Bakery, Opp Satyam Theatre, Ameerpet.

608/A, Nilgiri Block, Aditya Enclave, Beside Metro Station, Ameerpet.

, : +91-7095447721, +91-9533344772