Advanced certification in data science & AI (ACIDS) 2019-08-31T10:50:12+00:00

Advanced Certification In Data Science & AI (ACIDS)

Learn the most advanced concepts in Data science & AI with analytics tools R | Python | SAS, several other concepts for a better understanding of Artificial Intelligence.

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About Course

Data administration and management is the biggest challenges faced globally due to the increase in data every minute. Advanced Certification in Data Science & AI(ACIDS) course helps you understand data science & AI technology and also provides precise and accurate knowledge to become data science professionals. ACIDS course allows one to implement individuals basic data base knowledge and applies to the more advanced level of data science which is typically required for the current data analysis of the IT industry.

At Integrum Litera we begin with learning in detail about Introduction to data science, overview of data science, applications of data science, skills required for a data scientists, steps involved in a data Science project, descriptive and inferential statistics, basic machine learning algorithms, advanced machine learning algorithms, time series analysis and forecasting, statistical analysis tools (R,Python & SAS), deep learning, neural networks, Image & text processing & lab sessions led by professionals, hands on real time projects, exclusive placement assistance followed by mock interviews and certification.

CURRICULUM

Module 01: Introduction to Data Science

  • Introduction to Data Science
  • Life cycle of data science
  • Skills required for data science
  • Career path in data science
  • Applications of data science

Module 02: Statistics in Data science:

  • Introduction to Data:
    • Data types
    • Data Collection Techniques
  • Descriptive Statistics:
    • Measures of Central Tendency
    • Measures of Dispersion Measures of Skewness and Kurtosis
    • Visualization
  • Inferential Statistics:
    • Sampling variability and Central Limit Theorem
    • Confidence Interval for Mean Hypothesis Testing, t- Test, F-Test, Chi-square Test
    • ANOVA
  • Random Sampling and Probability Distribution:
    • Probability and Limitations, Discrete Probability, Continuous Probability
    • Bernoulli, Binomial Poisson distribution and Normal Distribution

Module 4: Statistical Learning:

  • What Is Statistical Learning?
  • Why Estimate f?
  • How Do We Estimate f?

1. The Trade-Off Between Prediction Accuracy and Model Interpretability

    • Supervised Versus Unsupervised Learning
    • Regression Versus Classification Problems
    • Assessing Model Accuracy
    • Measuring the Quality of Fit
    • The Bias-Variance Trade-Off

  • 2. Linear Regression:

    Simple Linear Regression:
  • Estimating the Coefficients
  • Assessing the Accuracy of the Coefficient Estimates
  • Assessing the Accuracy of the Model

3. Multiple Linear Regression:

  • Estimating the Regression Coefficients
  • Some Important Questions
  • Other Considerations in the Regression Model
  • Qualitative Predictors
  • Interaction Terms
  • Non-linear Transformations of the Predictors
  • Extensions of the Linear Model
  • Potential Problems

    4. Classification:
  • An Overview of Classification
  • Why Not Linear Regression

    5. Logistic Regression:
  • The Logistic Model
  • Estimating the Regression Coefficients
  • Making Predictions
  • Multiple Logistic Regression
  • Logistic Regression for >2 Response Classes

    6. Resampling Methods:
  • Cross-Validation:
  • The Validation Set Approach
  • Leave-One-Out Cross-Validation
  • k-Fold Cross-Validation
  • Bias-Variance Trade-Off for k-Fold
  • Cross-Validation
  • Cross-Validation on Classification Problems
  • The Bootstrap

    7. Linear Model Selection and Regularization:
  • Subset Selection
  • Best Subset
  • election Stepwise
  • Selection Forward and Backward Stepwise Selection
  • Choosing the Optimal Model

    8. Shrinkage Methods:
  • Ridge Regression
  • The Lasso Regression K-Nearest Neighbor

Module 5 : Deep dive into Machine Learning:

Tree-Based Methods

1. Basics of Decision Trees:

  • Regression Trees
  • Classification Trees
  • Trees Versus Linear Models
  • Advantages and Disadvantages of Trees

2. Bagging, Random Forests, Boosting:

  • Bagging
  • Random Forests
  • Boosting

3. Support Vector Machines:

  • Hyperplane
  • The Maximal Margin Classifier
  • Support Vector Classifiers
  • Support Vector Machines
  • Kernel Trick
  • Gamma, Cost and Epsilon
  • SVMs with More than Two Classes

Module 7: Unsupervised Learning:

  • The Challenges of Unsupervised Learning
  • Principal Components Analysis:
    • What Are Principal Components?
    • Another Interpretation of Principal Components
    • More on PCA
    • Other Uses for Principal Components


Clustering Methods:

  • K-Means Clustering
  • Hierarchical Clustering
  • Practical Issues in Clustering

Module 8: Association Rules Mining and Times Series Analysis

1. Association Rules Mining:

  • Market Basket Analysis
  • Apriori/Support/Confidence/Lift


2. Time Series Analysis:

  • What is Times Series Data?
    • Stationarity in Time Series Data
    • Augmented Dickey Fuller Test
    • The Box-Jenkins Approach
    • The AR Process
    • The MA Process
  • What is ARIMA?
  • ACF,
  • PACF and IACF plots
  • Decomposition of Times Series
  • Trend, Seasonality and Cyclic
  • Exponential Smoothing
  • EWMA

Module 9: Neural Networks, Deep Learning & Practical Issues:

Introduction to Neural Networks and Deep Learning:

  • Units/Neurons
  • Weights/Parameters/Connections
  • Single Layer Perceptron
  • Multilayer Perceptron

    Activation Functions:
  • Sigmoid
  • Tanh
  • ReLU
  • Leaky ReLU
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Image Processing
  • Natural Language Processing

Module 6: Python for Data Science

1. Python programming:

  • Environment Setup
  • Jupyter Notebook Overview
  • Data types:
    • Numbers
    • Strings
    • Printing, Lists,
    • Dictionaries, Booleans,Tuples ,Sets
    • Comparison Operators
    • if, elif, else Statements

2. Loops:

  • for Loops, while Loops
  • range()
  • list comprehension
  • functions

3. Python for Exploratory Data Analysis:

  • Numpy
  • Pandas

4. Python for Data Visualization:

  • Matplotlib
  • Seaborn
  • Pandas built in visualization
Module 3: R for R-programming

Introduction to R-programmingData Science:

  • Environment Setup
  • R Studio Overview
  • Introduction to R
  • Why R Data types in R Operators
  • Built in Functions

1.Different data structures:

  • Vector
  • Factor
  • Matrices
  • Arrays
  • Lists
  • Data frame


2.Data Import and Export:

  • Text
  • Csv
  • Excel

3.Control Structures in R:

  • If , If else, for
  • While
  • Repeat
  • Break
  • Next
  • Stop

4.Functions in R:

  • In-built functions
  • User defined functions


5.R for Data Visualization:

  • Line Plot
  • Bar Chart
  • Pie Chart
  • Histogram
  • Scatter plot
  • Box Plot

Course Details

The Advanced certification program is delivered is the most pragmatic learning approach which is an interfusion of theoretical & practical learning to ensure the participants comprension is accurate.

• Technology infused learning
• 24*7 access to curriculum & access to case studies & data sets
• Guest Lectures by Industry experts
• Hackathons & Real time projects
• A most friendly & supportive environment

Case Studies:

Education industry using Linear Regression in R

Insurance domain using Logistic Regression in R

Banking Industry using Decision Tree in R

Network Intrusion using Decision tree in R

Manufacturing industry Support Vector Machine in R

Salary Analysis using Lasso and Ridge Regression in R

Liquor Industry using Clustering in R

Recommendation system for movies using KNN in Python.

Text Analystics using Bag of words and multi-class classification in Python.

Stochastic Gradient Descent method using Mini Batch Algorithm in Python

Hand written images using clustering in Python

Mahabharata using Bag of words in Python.

Face Recognition using SVM in Python

Weather and pollution analysis using Times Series in Python.

Hand written digits using Auto Encoder Neural Network in Python

Advanced certification in data science

In 2012, Harvard Business Review named data scientist the “sexiest job of the 21st century.” More recently, Glassdoor named it the “best job of the year” for 2016.

“It isn’t a big surprise,” Dr. Andrew Chamberlain, Glassdoor’s chief economist, told Business Insider. “It’s one of the hottest and fastest growing jobs we’re seeing right now.”

acids

According to Glassdoor, data scientists earn a base pay of $116,840 a year, on average.

Here’s how much they take in, on average, at some of the hottest tech companies, according to Glass-Door’s employee salary reviews:
Apple: $149,963
LinkedIn: $138,798
Facebook: $133,841
Twitter: $134,861
Microsoft: $119,129
Airbnb: $117,229

The advanced certification program is perfect for the participants who are very keen on working towards analytics, automation, AI & to enhance their skillset in the most advanced technology in the world.

1. Why be a Data Scientist?
Data scientist is the peak rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are limited and in great ultimatum. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.

2. What projects are included in this program?
This Data Science advanced program includes multiple real-lives, industry-based projects on different domains to help you master concepts of Data Science and Artificial Intelligence including 15 – 20 case studies.

3. What kind of jobs that I can get?
Jobs that are ideal for data science-trained professionals include:

  • Statistical programming specialist
  • Data analyst
  • Data scientist
  • Data science manager
  • R analyst
  • Python programmer
  • Business Analyst

4. What is Placement assistance program?
Integrum Litera’s Placement assistance program is an offering in Partnership with multiple MNC’s to help you land your dream job. With Placement assistance program we offer extended support for the certified learners who are looking for a job switch or starting with their first job. Upon successful completion of the advanced certification Program, you will be eligible to apply for this program and your details will be shared with our Placement officer.

  • Resume building assistance
  • Career Mentoring
  • Interview Preparation
  • Career Fairs
  • Relevant Interview opportunities

5. Who should take this course?
The data science role requires the perfect fusion of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:

  • IT professionals
  • Analytics Managers
  • Business Analysts
  • Banking and Finance professionals
  • Marketing Managers
  • Supply Chain Network Managers
  • Those new to the data analytics domain
  • Students in UG/ PG Analytics Programs
  • B-tech students
  • Aspirants who are good with math, computers & statistics

6. I am a fresher; will I be eligible for placement assistance program?
Any graduate who has successfully completed our advanced certification Programs will be eligible to participate in the placement assistance Program. 1+ years of experience is however recommended, if you are fresher also, that shouldn’t be a problem.

7. Can I choose not to go for the placements and avail discount instead?
No, the placement assistance Program is a complementary offering which comes along with the Advanced Certification Programs. It will make your probabilities high to get hired by the chief companies.

8. Is this LIVE training or prerecorded classes?
At Integrum Litera, we do not provide any sort of self-paced e-learning courses. Currently we only provide Classroom & Online training. Our Online sessions are Instructor LED live sessions, conducted using the most advanced technology in the market. At completion of each session we share the recorded videos with the participants. Hence, you will have access to live training conducted online as well as the pre-recorded videos.

9. What is covered under the 24*7 Support?
We offer 24/7 support through emails & chat. To speak to our program advisors, you can connect with us via a call from Monday to Saturday from 10:00 AM to 06:00 PM.

10. What if I miss a session?
We provide recordings for each class after the session is conducted. If you miss a class, you can go through the recordings before the next session.

11. Where and how do I access e-learning content?
Once you have registered and paid for the course, you will have 24/7 access to the eLearning content on our website for a full 365 days. You will receive a course purchase confirmation receipt by email that will guide you through the process of our Advanced Certification Program in Data science & AI.

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