#### Course Description

ExcelR Solutions, steered by a team of academically qualified and technically experienced professionals in various emerging technologies, have developed a long term program on Data Science with various emerging technologies to make the students truly.

So it is time to " train while you learn"

• As part of this program, a part of the most advanced curriculum, the students will be exposed to the various live project from industry so that they can seamlessly transition to being an employee from being a student
• ExcelR with its partner firm offers internship opportunities to meritorious students in their chosen domain in Data Science
• Our dynamic curriculum is an amalgamation of the latest emerging technologies that will arm a student with more than necessary skills to succeed in the job market.

#### SEMESTER 1

• Installation
• R Studio
##### 2.Introduction to Python and IDE for python
• Installation
• IDE for Python
##### 3.Basic statistics using R and Python
• Introduction to data
• Data types
• Measuring data
• Probability
• Probability applications with examples
• Probability Distribution
• Types of Probability Distribution
• Examples of Probability Distribution
• Inferential Statistics
• Sampling Technique
• Different Types of Sampling Technique
• SRS technique
• Measure of Central Tendency
• Measure of Dispersion
• Shape Statistics
##### 4.Graphical Representation in R and Python
• Visualization
##### 5.Probability Distribution
• Continuous Probability Distribution
• Introduction to Normal Distribution
• Properties of Normal Distribution
• Standard Normal Distribution (Z dist)
• Student t-Distribution
• Chi-Square Distribution
• Poisson Distribution
• Logarithmic Distribution
• Binomial Distribution
• Sampling Variation
• Central Limit Theorem
• Normal Q-Q plot and its Interpretation
• Confidence Interval
• P-value
• Confidence level calculations
##### 6.Hypothesis Testing using Minitab, R & Python
• Introduction to Hypothesis
• Inferential statistics
• Introduction to Minitab tool
• Framing Hypothesis statement
• Type I error
• Type II error
• Methods to deal with Non-Normal data
• Types of Hypothesis testing
• Case studies using Minitab, R
• Fundamentals of Finance
• Marketing and CRM
##### 8.Principles of Lean Six Sigma
• DMAIC methodology & Minitab
• Introduction to SIX SIGMA
• Team Management
• Define Phase
• Measure Phase
• Analyze Phase
• Improve Phase
• Control Phase
• Design for Six Sigma (DFSS) Frameworks and Methodologies
• Six Sigma Green Belt certification
##### 9.Regression Analysis using R and Python
• Y as function of X
• Relation between Dependent and Independent Variable
• Evaluating the relation using Scatter plot
• Measure of Correlation – Using Correlation coefficient
• Correlation Coefficient and its Analysis
• Equation of Straight Line
• Regression model using “Ordinary Least Square”
• Coefficient of Determination
• Prerequisites of Regression
• Types of Linear Regression
• P-values and coefficients interpretation
• F-Statistic and p-value
• Methods to increase Accuracy
• Error interpretation (RMSE)
• Predicting Binary Output (Y is binary)
• Logit and Probit
• Confusion Matrix
• ROC curve
• Lift chart
• Log Likelihood
• Measure of Accuracy using Confusion Matrix
• Model Improvement Techniques
• Predicting Nominal data (> 2 category)
• Predicting Count data
• Introduction to Bias and Variance
• Lasso Regression
• Ridge Regression
• Imputation – Handling missing data
• Zero-Inflated regression – Handling excess zero’s

#### SEMESTER 2

##### 1.Data Mining using R and Python
• Supervised
• Unsupervised
• Regression Analysis
• Survival Analysis using R & Python
##### 2.Data Mining Unsupervised using R and Python
• Introduction to big data
• Clustering
• Types of Clustering
• Clustering applications and its Limitations
• Cluster Modeling using Tableau
• Dimension Reduction
• Affinity Analysis / Association Rules
• Recommendation Engine
##### 3.Data Mining Supervised using R and Python
• Classification / Pattern Mining
• Black Box
##### 4.Data Collection
• Primary data
• Secondary data
• Conduction survey’s in order to collect data
• Digital vs Traditional collection methods
• Web extraction – extracting data from social media
##### 5.Text Mining using R and Python
• Introduction to Text Mining & NLP
• Corpus / Corpora
• Documents
• Factorizing Data
• Bag of Words
• Document Term Matrix / Term Document Matrix
• Normalizing frequency using TFIDF
• Word Cloud
• N-gram word cloud
• Letter Cloud
• Positive Negative Words
• NLP
• Introduction to parts-of-speech tagging
• Perceptual map/bi-plot
• Trend tracking – topics across time
• Sentence & Word annotations
• Named entity annotations
• Content Analysis
• Lexicons
• Emotion Mining – Arcs & emotions
• Use of machine learning in text classification
##### 6.Forecasting using XLMiner, R & Python
• Introduction to Time series
• Difference between Cross-sectional data and Time series data
• Steps involved in Forecasting
• Time Series Components
• Types of Visualizations
• Autocorrelation and Standard error
• Forecasting Error
• Forecasting Methods
• Fit the best model for 100% data
• Forecast for Future values
##### 7.Contemporary Analytics
• Understanding Business before designing a strategy
• SEO Basics
• Social Media Optimization
• Web Analytics
• Online Reputation management
• Email Marketing
• App Store Optimization
##### 8.Visualization Tools
• NodeXL
• Tableau
• Tableau Certification (optional)

#### SEMESTER 3

##### 1.Advanced ML topics using R & Python
• Boosting & Bagging
• C5.0
• Bias & Variance
• Regularization
##### 2.Deep Learning using R & Python
• Deep feedforward networks or Multilayer perceptron’s
• Performance of Deep Learning Models
• Image Processing models: Convolutional Networks
• Sequence Modeling: Recurrent and recursive networks
##### 3.Artificial Intelligence
• Image Filtering
• Edge Detection origin f edges
• Frequency Domain
• Image sub-sampling
• Image Features Detection
• Image Feature Descriptors
• Feature Matching
• Window-based Models for Category Recognition
• Neural Network
• Spark
##### 5.Agile PM program
• Agile concerns & issues
• Introduction to various agile methodologies
• Eight principles of Agile project management
• Testing concepts in DSDM Atern
• Configuration management in DSDM Atern
• Different agile project management styles
• Project development framework
• Different phases of DSDM Atern
• Agile Control
• Risk Management
• Agile Requirements
• Estimating & Measurement
• Planning Agile Projects & Planning Considerations
• Agile Quality Management
• Outline Plan
• Delivery Plan
• Deployment Plan
• Timebox Plan

#### SEMESTER 4

##### SEMESTER 4
• Case studies
• POC
• Capstone project

Contact Our Team of Experts

FAQs

Global Presence

ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.