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Course Description

About Advanced Analytics Certification Training

ExcelR offers 60 hours of classroom training on Advanced Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”. The training is conducted by alumni of premier institutions such as IIT & ISB who has extensive experience in the arena of analytics. They are considered to be one of the best trainers in the industry. The topics covered as part of this Data Scientist Certification program is on par with most of the Master of Science in Analytics (MS in Business Analytics / MS in Data Analytics) programs across the top-notch universities of the globe.

Our Advanced Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who want to pursue a career as a Data Scientist in the job market. We offer a comprehensive placement program where we equip you with hands-on training on Business Analytics, resume preparation, case studies, Live projects, mock interviews, etc. We do the necessary hand-holding until the participants are placed in a job in the field of Analytics

  • Course content is designed & training is delivered by IIT, ISB alumni who have immense experience in building AI and Deep Learning system solutions
  • Algorithms and concepts will be explained with a blend of theory & practicals by including a real-life case study for each concept explained
  • On successful completion, of course, participants will get an opportunity to take part in designing solutions & implementing the same to solve real-world problems
  • Learn the de-facto tools used in the space of deep learning & advanced analytics. Tools include Python, R, XLMiner, Minitab, @Risk, OpenCV
  • Complete support on the first live-project, you work at your workplace

About Advanced Analytics Certification Training

Gartner, a top-notch technology research firm, defines advanced analytics as to the process of examination of huge volumes of data using sophisticated techniques & tools with or without human intervention, to discover deeper insights!

It is quite unlikely to be able to perform these tasks using traditional approaches for drawing deeper insights on the humongous, find pattern recognition from the complex, hidden data

In dealing with ‘Big Data Analytics’ where raw data is largely unlabelled and uncategorized, we must have tools and data visualization technologies along with advanced analytical skills & expertise in deep learning to be able to solve problems and find new opportunities.

Organizations are collecting enormous amounts of domain-specific data from various sources of live-feed.

Crunching data of this magnitude needs special skills & analytical capabilities.

Using Advanced Analytics and Deep Learning techniques one can solve a lot of complex problems that are otherwise not possible. A few such examples are:

  • Research in areas where human lives are at high risk can be avoided with the application of Deep Learning concepts
  • Reducing manual intervention in some core areas would result in improved productivity is quality
  • Fraud detection can be done using Deep Learning, helping many organizations take appropriate strategic decisions in the least amount of time
  • Using Neural Networks, customer recommendations are efficiently carried out
  • Image processing and tagging, Voice recognition system, search engines, customer recommendations, customer relationship management, are all areas which can be improved using advanced analytical techniques
  • Live-feeds is being dynamically read and understood by deep learning concepts

Industries are on a lookout for resources with Advanced analytics skills who have hands-on experience working with Deep Learning algorithms. There is a huge concern about the lack of skilled resources with Advanced analytics and Deep Learning skill-set.

According to the report published by Zion Market Research, the estimated growth for advanced analytics in the global market would be around 50% by 2021. This is a huge increase in market value since 2015 when the market value was around USD 10.70 billion.

Course Curriculum

Deep Learning and Artificial Intelligence

  • Boosting & Bagging
    •  Intro
    •  What is Bagging and Boosting
    •  Comparing the results of Boosting and a single model
    •  Parameters in Boosting
  • Gradient Descent
    • Intro
    • Concepts of Gradient Descent
    • Cost function
    • Learning rate
  • Extreme Gradient Boosting (XGBM)
    • Intro
    • Concepts of XGBM
    • Parameters in XGBM
    • Implementation of XGBM
  •  C5.0
    • Intro
    • Concepts of C5.0
    • Entropy
    • Information Gain
    • Forward Pruning
    • Backward Pruning
    • Implementation of C5.0
  • Bias & Variance
  • Regularization
  • Deep feedforward networks or Multilayer Perceptrons
    • Intro
    • Neurons
    • Neuron Weights
    • Activation Function
    • Networks of Neurons
    • Input or Visible Layers
    • Hidden Units
    • Output Layer
    • Architecture Design
    • Gradient-Based Learning
  • Performance of Deep Learning Models
    • Empirically Evaluate Network Configurations
    • Data Splitting
    • Use an Automatic Verification Dataset
    • Use a Manual Verification Dataset
    • Manual k-Fold Cross-Validation
  • Advanced Multilayer Perceptron
  • Image Processing models: Convolutional Networks
    • Convolutional Layers
    • Filters
    • Feature Maps
    • Pooling Layers
    • Downsampling
    • Fully Connected Layers
  • Sequence Modeling: Recurrent and recursive networks
    • Long Short-Term Memory (LSTM) Networks
    • Time Series Prediction with Multilayer Perceptrons
    • Time Series Prediction with LSTM
    • Recurrent Neural Networks
  • Maths behind Optimization
    • Introduction to derivatives
    • Derivatives in optimization – Maxima & Minima
    • Application of optimization in arriving at Linear Least Squares
    • Gradient Descent Optimization
  • Linear Programming
    • Introduction to Linear programming
    • Formulating linear programming models
    • Solving linear programming models
    • Understand resource allocation problems
    • Understand cost-benefit analysis problems
  • Duality & other analysis
    • Decision variables, constraints & objective function
    • Duality problems
    • Sensitivity analysis
    • Network Analysis
    • Transportation, Shortest path, Maximal flow problems
    • Introduction to integer linear programming
    • Introduction to Non-linear optimization
  • Introduction to Probability
    • Review of probability
    • Conditional Probability
    • Bayes theorem
    • Permutations & Combinations
  •  Introduction to Probability Distributions
    • Bernoulli
    • Binomial
    • Geometric
    • Negative Binomial
    • Poisson
    • Uniform Distribution
    • Triangular
    • Exponential
    • Normal
  • Introduction to Simulation
    • Basics of simulation
    • Statistical sampling
    • The case study on the application of simulation
  •  Bidding
  • Marketing
    • Fitting distributions to data
    • Decision Tree Simulation
    • Discrete Event Simulation
    • Queuing Theory
  • Introduction to DOE
  • Introduction of DOE terms
    • Factor, Level, Treatment, Treatment combination
    • Blocking, Center points, Repetition, Replication
    • Main effects, Interaction effects
  • Types of experiments
    • Trial & Error
    • One-Factor-At-A-Time (OFAT)
    • Full factorial design
    • Fractional factorial design
  •  Phases of DOE
    • Screening
    • Characterization
    • The 7-step process
    • Balanced DOE
    • Calculation of main & interaction effects
    • Creation of designed experiments
    • Power & Sample size
    • Blocking
  • Defining a custom design
  • Checking model assumptions
  • Full factorial results analysis
  • DOE model reduction
  • DOE main effect & interaction effect plots
  • Cube plot, Contour & surface plots
  • Fractional factorial design
    • Confounding
    • Folding
  • Randomized blocks & Latin square
  • Implementation plan
  • Introduction to Text Mining & NLP
  • Factorizing Data
  • Introduction to topic models
  • Latent topic modeling
  • 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 survival analysis
  • Time-to-event data
  • Censoring & types of censoring
  • Survival Analysis Techniques
    • Single group (Nonparametric methods)
  •       Life Table
  •       Kaplan-Meier
  •       Nelson-Aalen cumulative hazard estimation
    • Comparison of groups
  •       Log-rank test
  •       Wilcoxon test
    • Semi-parametric estimation mode
    • Cox proportional hazard model
  • Survivor function & Hazard function
  • Bathtub curve
  • Comparison of survival curves
  • Failure time distributions
    • Weibull
    • Gompertz
    • Log-logistic
  • Accelerated event-time
  • Customer lifetime value
  • Installing & setting up Spark locally
  • Spark programming in Python
  • Designing a machine learning system
  • Obtaining, processing & preparing data with Spark
  • Building a recommendation engine with Spark
  • Building a classification model with Spark
  • Building a regression model with Spark
  • Building a clustering model with Spark
  • Dimensionality reduction with Spark
  • Advanced text processing with Spark
  • Real-time machine learning with Spark streaming

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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.

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