Data Science Certification Course Training Institute in Hyderabad - Digital Lync

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Data Science Certification Training

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Why Should You Take Data Science?

  • Enormous amount of data will be used to drive key business decisions and skilled data scientists will be the key to unlocking the endless possibilities.
  • Data-scientist salaries are 113% more than average salaries for all job postings, according to Indeed.com. The average data scientist salary is $113,436, according to Glassdoor.
  • There 50 percent year-over-year rise in a job listing for Data Science signifying potential job opportunities.

Course Curriculum

It Stretches Your Mind, Think Better And Create Even Better.
1

Introduction

High level view of Data Science, Artificial Intelligence & Machine Learning

Subtle differences between DS, ML & AI

Approaches to ML

Approaches to ML

Terms & terminologies of DS

Ideas of Pipe line, implementation cycle

2

Statistics

Measures of Central Tendency (Mean, Median, Mode)

Measures of Central Tendency (Mean, Median, Mode)

Dispersion (Variance, Standard Deviation)

Types of Distributions

Scatter plot

Box whisker plot

Qualitative ideas of

Statistical sampling & inference

Hypothesis Testing & t-tests

Confidence Intervals

Prerequisites of above ideas(qualitative)

Terms , Terminology & Notions of Linear Algebra Relevant to Data Science including Probability

3

PipeLine ideas

Exploratory Data Analysis

Feature Creation

Evaluation Measures

Data Analytics Cycle ideas

Data Acquisition

Data Preparation

Data cleaning

Data Visualization

Model Planning & Model Building

Different Errors (MAE, MSE, RMSE)

Confusion Matrix, Precision, Recall

4

Python for Data Science

Numpy

Pandas

Matplotlib & Seaborn

Jupyter Notebook

Basics of preprocessing with

Numpy

Basics

Loading with numpy

Slicing

Matrix operations

Pandas

Basics

Loading with pandas

Descriptive statistics with both of above

Matplotlib(data visualization)

Boxplots & scatterplots generation

Data Inputting

Reading and writing data to text files save

Reading data from a csv save

Reading data from JSON

Data preparation(following are taught explicitly first and then used, not using in code)

Transform

Rescale

Standardize

Normalize

binarize

One hot Encoding

Imputing

Train

Test Splitting

5

Data preprocessing on customer purchase data to handle missing data, transformation, encoding and Train Test splitting.

6

Classification methods & respective evaluation

K-nn

Decision trees

Naive Bayes

Stochastic Gradient Descent

SVM

Linear

Non linear

Random Forest

XGboost

Logistic regression

Ensemble methods

Combining models

Bagging

Boosting

voting

Choosing best classification method

Dimensionality reduction(with minimal theory)

PCA(usually projected as part of unsupervised)

LCA

LDA

Model Tuning

K-fold cross validation

Variance bias tradeoff

L1 and L2 norm

Overfit, underfit along with learning curves variance bias sensibility using graphs but not code

Regression

Linear Regression

Variants of Regression

Lasso

Ridge

Multi Linear Regression

Logistic Regression (effectively, classification only)

Regression Model Improvement – Tips and Tricks

7

Clustering

K means

Hierarchical Clustering

Association Rules(market basket analysis)

8

Supervised Learning

Social Network Ads based Prediction

Predicting if a user buys a specific product or not based on the ad populated on the Social Network. Data uses specific demographic information about the user to

Churn Analysis in Telecommunication

A customer can be called as a “churner” when he/she discontinue their subscription in a company and move their business to a competitor. Prediction as well as prevention of customer churn brings a huge additional revenue source for every business.

Here, we use a telecom customer data set to classify the set of possible customers who are likely to churn

Predicting Housing prices using Regression

Predict the sales price for each house based on input features provided for the house.

Unsupervised Learning

Retail Customer segmentation based on spending patterns

Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their purchase patterns helps Retail companies to cluster their user base and serve them effectively.

Market Basket Analysis

Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together.The technique determines relationships of what products were purchased with which other product(s).

9

Time series

Time series Analysis.

ARIMA example

Recommender Systems

Content Based Recommendation

Collaborative Filtering

Text analytics

Natural Language Processing

Stemming, Lemmatization and Stop word removal.

POS tagging and Named Entity Recognition

Bigrams, Ngrams and colocations

Term Frequency and TF-IDF

DevOPS for Data Science

Tasks in Data Science Development

Deploying Models in Production

Deploying Machine Learning Models as Services

Running Machine Learning Services in Containers

10

SMS Spam Detection

In our day-to-day lives, we receive a large number of spam/junk messages either in the form of Text (SMS) or E-mails. It is important to filter these spam messages since they are not truthful or trustworthy.

In this case study, we apply various machine learning algorithms to categorize the messages depending on whether they are spam or not.

Sentiment analysis on Restaurant Reviews using Natural Language processing and Supervised Learning

It involves in identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.

11

ANN

NN & terminologies

Non linearity problem, illustration

Perceptron learning

Feed Forward Network and Back propagation

Gradient Descent

Additional relevant Mathematics

Gradients

Partial derivatives

Linear algebra

Li

LD

Eigen vectors

Projections

Vector quantization

Overview of

Tensor Flow

Keras

Deep Learning with Convolutional Neural Nets

Architecture of CNN

Types of layers in CNN

filters

Building an Image classifier with and without CNN

Recurrent neural nets

Fundamental notions & ideas

Recurrent neurons

Handling variable length sequences

Training a sequence classifier(ideas)

Training to predict Time series

Reinforcement Learning(overview)

Autoencoders(overview)

12

Deep Learning

Tensorflow & keras installation

6 basic tensorflow examples

Sales Prediction of a Gaming company using Neural Networks

One or two neuro lab fundamentals on neural nets

Build an image similarity engine

CNN thread bare discussion + 2 examples(tf & keras separately) +2 assignments+CIFAR10 code +cifar 100 discussion

RNN 2 different implementations with threadbare discussion

1 GRU example

Some minimal discussions of

More elaborate discussion on cost function

Measuring accuracy of hypothesis function

Role of gradient function in minimizing cost function

Explicit discussion of Bayes models

HMM

Optimization basics

Scope of considering Computer vision examples

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curriculum

Stock market price prediction

This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors vs. psychological, rational and irrational behavior etc. Machine learning techniques implemented in Python acts as game changer for the predictions. Algorithms including Linear regression, LSTM and ARIMA model are used for the same.

Exploratory Data analysis of Crime records in Boston

This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the years. Exploratory Data Analysis is carried on the crimes data using lots of techniques from Linear model to Stochastic gradient boosting.

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