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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.
"Future Ready"
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.
Course Curriculum
SEMESTER 1
- Installation
- R Studio
- Installation
- IDE for 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
- Visualization
- 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
- 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
- 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
- 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
- Link function
- 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
- Supervised
- Unsupervised
- Regression Analysis
- Survival Analysis using R & 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
- Classification / Pattern Mining
- Black Box
- Primary data
- Secondary data
- Conduction survey’s in order to collect data
- Digital vs Traditional collection methods
- Web extraction – extracting data from social media
- 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
- 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
- Understanding Business before designing a strategy
- SEO Basics
- Algorithms & Backlinks
- Social Media Optimization
- Web Analytics
- Online Reputation management
- Email Marketing
- App Store Optimization
- NodeXL
- Tableau
- Tableau Certification (optional)
SEMESTER 3
- Boosting & Bagging
- Gradient Descent
- Extreme Gradient Boosting (XGBM)
- C5.0
- Bias & Variance
- Regularization
- Deep feedforward networks or Multilayer perceptron’s
- Performance of Deep Learning Models
- Advanced Multilayer Perceptron
- Image Processing models: Convolutional Networks
- Sequence Modeling: Recurrent and recursive networks
- 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
- Introduction to Hadoop
- Components of Hadoop
- Hadoop Eco-system tools
- Spark
- 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
- Case studies
- POC
- Capstone project
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