Data Science

Data Science

Data Science Training in Kphb Hyderabad

 

data science institutes in kphb
data science institutes in kphb

Data Science with Python Training Overview

Data Science AI Deep Machine Learning  training in Hyderabad. It is developed with data science tool and which is used to simplify and easily access the data and store the data easily. By R Programming language we can easily manipulate the data, also it can help in the analysis of Data, we can create the wonderful visualization and helps to access the high-quality content. This data science course in Kukatpally with Python Training provides you to learn data manipulation and cleaning of data using python.

Objectives of the Data Science Course in Kukatpally

Complete basics of Data Science Course in Kukatpally
The concepts of BigData and able to work in Data mining.
Understand the usage and how to use the tools like a tableau, map-reduce…

Pre-requisites of the data science course in kukatpally

IT experienced Professional who are interested to build their career in development/ data scientist.
Any B.E/ B.Tech/ BSC/ MCA/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.

Who can attend this data science with python training in hyderabad

The course can learn by any IT professional having basic knowledge of:

Mathematics
Statistics
Any Programming Language

,data science institutes in kukatpally,data science course in kukatpally, data science with python in hyderabad

 

Course Details:

Note: This course is for the students with basic knowledge on any other programming language.

Duration:  10 weeks

Time : 8am or 7pm

Mode : class room

Course Fee:Contact Us

 

Trainer :

name: RAJESH

Qualification            : B.Tech, IIIT Hyderabad
Experience              : Software professional with 10 yrs
Courses                    :Data Science PYTHON

 

Course Content

SNO TOPIC SUB TOPIC duration
0 Machine Learning introduction
1 Introduction to Statistics Mean, Mode

Variance

covariance

Standard deviation

Correlation Coefficient

  • Statistical Inference concepts:
  • Hypothesis Testing
  • p-values
  • Types of Data Distributions
  • T-test,
  • Z-test,
  • Chi-Square,
  • ANOVA test
  • Correlation,
  • Covariance
  • Central Limit Theorem & Probability
 1 week
2 Python  

 

data structures

drawing graphs

Numpy

Pandas

sklearn

 

 

 

 Lists,Tuples,sets,dictionaries

Scatter, plot, Pie and Bar

arrays,matrix,statistics

series,frames,read csv/excel

Data sets

Apply Data Frame functions

 

 2 week
Data Analysis data processing

 

 

 

 

 

Feature scaling

 

TensorFlow

 read data from csv/excel sheet

Extract rows/ columns

update df with new cols

identify NAN values

identify invalid data

predict the value and replace

 

 

 

 

Introduction to TensorFlow

Introduction to Tensor with Tensorflow

Linear Regression Using Tensorflow

 

 1 week
3 Regression Linear regression

Multiple regression

Polynomial Regression

 

 1 week
4 Classification logistic regression

Naïve Bayes Classifier

K nearest neighbors

Support Vector Machines (SVM)

Decision Tree

random forest

Time Series Analysis – ARIMA, Auto ARIMA

 1 week
5 Clustering K means clustering
6 Model Selection Principal Compenent Analysis (PCA)

Recommendation Systems
7 Natural language processing NLP Text Preprocessing

Word tokenize

Stop words

Stemming

Part of speech tagging

Chunking

llemmatizing and  corpora

Word net

Topic Modeling

Sentiment Analysis

Vector Space Models: Word2Vec, GloVe

 

 1 week
8 Deep Learning Neural networks

Single Layer & Multi-Layer Perceptron

Forward Propagation

Types of Activation functions

Bias & Weights

Optimization Techniques: RMSprop, Adam, Adagrad

Architectures:

Convolutional Neural Networks, CNN

Recurrent Neural Networks, RNN & LSTM

 1 week
9

Statistics:

  1. Statistical Inference concepts: Hypothesis Testing, p-values, Types of Data Distributions
  2. T-test, Z-test, Chi-Square, ANOVA test
  3. Correlation, Covariance
  4. Central Limit Theorem & Probability

Supervised Models:

  1. Linear & Logistic Regression
  2. Decision Trees
  3. Ensemble Models
    1. Bagging - Random Forest
    2. Boosting – Adaboost, Gradient Boost, XGBoost, LightGBM
  4. Support Vector Machine
  5. Naïve Bayes Classifier
  6. K Nearest Neighbor (KNN)
  7. Time Series Analysis – ARIMA, Auto ARIMA

Unsupervised Models:

  1. Clustering
  2. Dimensionality Reduction: Principal Component Analysis
  3. Recommendation Systems

Natural Language Processing – NLP:

  1. Text Preprocessing- CountVectorizer, TfIdfVectorizer, Stop word Removal…
  2. Topic Modeling
  3. Sentiment Analysis
  4. Information Retrieval
  5. Vector Space Models: Word2Vec, GloVe

Deep Learning:

  1. Neural Networks: Single Layer & Multi-Layer Perceptron
  2. Forward Propagation
    1. Types of Activation functions
    2. Bias & Weights
    3. Optimization Techniques: RMSprop, Adam, Adagrad
  3. Architectures:
    1. Convolutional Neural Networks, CNN
    2. Recurrent Neural Networks, RNN & LSTM

Miscellaneous Concepts:

  1. Exploratory Data Analysis
  2. Feature Selection & Feature Extraction
  3. Feature Engineering
  4. Regularization & Hyperparameter Tuning using Random Search, Grid Search Cross Validation

Tools:

  1. Python – Jupyter Notebook & Pycharm
  2. GitHub