Data Science Bootcamp

Course Curriculum

 

Mode of Training Delivery: Online, Demo’s and Labs


Course Certification is from MSME (Govt. of India – Technical Skill creation) 


Syllabus as per Indian Statistical Institute – Data Analytics course curriculum

Course Fees: INR 25,000/-  (All Inclusive)


No cash payment or 3rdParty Payment through individuals is allowed. Participants are requested to pay by cheque or account transfer to ANALYTICS DOMAIN, payable at Pune. 

Mode of Payment:  

- Drawing a Cheque/DD in favor of “ANALYTICS DOMAIN” payable at Pune. 

For Account Transfers such as RTGS or NEFT: 

Current account number 0102102000026707 with IDBI BANK, Hadapsar, Pune; IFSC No. IBKL0000102. 

Organization Name: ANALYTICS DOMAIN


Evaluation: Assignments (4 X 10 = 40) + Presentation (10 Marks) + Project Work (50 Marks).  Free Training Material and Script Access

Participants must furnish 

1. Id proof such as Aadhar Card or Pan Card or Driving License

2. College Id card scanned copy. 

3. Resume if Internship opportunity required

Pre-requisites:  

1. Enthusiasm to Re-skill and pick up new learnings

2. Working or knowledge of Excel or one programming language is preferable

3. PC or laptop with at least 4GB RAM and internet facility  

Course Outline


Module 1: Introduction to Data Analytics

Use Case of Data Analytics

· Introduction to Data Analytics

· Understanding your Data

· Clarifications of jargons such as Data Mining, Business Analytics, Functional Analytics, Machine Learning, Artificial Intelligence and the likes of Data Science Facilitating good analytical thinking with data visualization

· Installation of Software for Python, R and creating AWS Cloud server

· Basics of Python and R programming


Module 2: Understanding your data

Statistical Concepts

· Descriptive Statistics using Advanced Excel, R and Python

- Probability using Python and R

· Concept of lift, Support, Confusion Matrix, Sensitivity and Specificity and their illustrations in Business Analytics

· Distribution Functions

· Markov Chains and usage

· Simulation concepts


Module 3: Introduction to R

Exploratory Data Analysis with R

· Loading, querying and manipulating data in R

· Cleaning raw data for modelling

· Reducing dimensions with Principal Component Analysis

· Extending R with user–defined packages

Facilitating good analytical thinking with data visualization

· Investigating characteristics of a data set through visualization

· Charting data distributions with boxplots, histograms and density plots

· Identifying outliers in data


Module 4: Unstructured Data working with Python

Mining unstructured data for business applications

· Preprocessing unstructured data in preparation for deeper analysis

· Describing a corpus of documents with a term–document matrix

· Make predictions from textual data

· Text Analytics


Module 5: Predicting Outcomes with Regression Techniques using Advanced Excel, Python and Knime

Estimating future values with linear regression

· Modelling the numeric relationship between an output variable and several input variables

· Correctly interpreting coefficients of continuous data

· Assess your regression models for ‘goodness of fit’


Module 6: Categorizing Data with Classification Techniques using Knime, Python and R

Automating the labelling of new data items

· Predicting target values using Decision Trees

· Constructing training and test data sets for predictive model building

· Dealing with issues of overfitting

Assessing model performance

· Evaluating classifiers with confusion matrices

· Calculating a model’s error rate


Module 7: Detecting Patterns in Complex Data with Clustering and Social Network Analysis

Identifying previously unknown groupings within a data set

· Segmenting the customer market with the K–Means algorithm

· Defining similarity with appropriate distance measures

· Constructing tree–like clusters with hierarchical clustering

· Clustering text documents and tweets to aid understanding

Discovering connections with Link Analysis

· Capturing important connections with Social Network Analysis

· Exploring how social networks results are used in marketing


Module 8: Leveraging Transaction Data to Yield Recommendations and Association Rules

Building and evaluating association rules

· Capturing true customer preferences in transaction data to enhance customer experience

· Calculating support, confidence and lift to distinguish "good" rules from "bad" rules

· Differentiating actionable, trivial and inexplicable rules

Constructing recommendation engines

· Cross–selling, up–selling and substitution as motivations

· Leveraging recommendations based on collaborative filtering


Module 9: Learning from Data Examples with Deep Learning

Deep Learning intricacies 

· Learning the weight of a neuron

· Learning about how neural networks are being applied to object recognition, image segmentation, human motion and language modelling

· Analyzing labelled data examples to find patterns in those examples that consistently correlate with particular labels for object recognition


Module 10: Implementing Analytics within Your Organization

Expanding analytic capabilities

· Breaking down Data Analytics into manageable steps

· Integrating analytics into current business processes

· Reviewing Hadoop, Spark, and Azure services for machine learning

Dissemination and Data Science policies

· Examining ethical questions of privacy in Data Science

· Disseminating results to different types of stakeholders

· Visualizing data to tell a story


Data Science Bootcamp Tools 

(Skills normally picked up by trainees during the Training)

Advanced Statistics

ETL Tools exposure 

Data Bases and Data warehousing

Machine Learning Skills, Deep Learning

R and its libraries

Python and its libraries

Advanced Excel

Big Data Analytics

Predictive analytics tools

Text Analytics Tools – Beautiful Soup

Social Media Analytics Tools

Web Scrapping tools… and lot’s more…

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