Data Science with cloud computing on AWS

Price 30000 | USD $900

Live class will start from 23 Oct 2021.

Attend 1 demo sessions free and pay only after you are satisfied.

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About Course

Masters in Data Science led by industry experts to help you learn the applications of data science from scratch and build as well as deploy powerful models to get useful business insights and forecasting. It has been designed for freshers and budding professionals who are looking to build their career in Data Science & Analytics. Herein you will learn various things from the beginning like basic to advanced python, visualization, web scraping, API, deployment on Aws & Heroku, database( Mongo DB), Full statistics, all machine learning algorithm, sentiment analysis by NLP, Time series by ARIMA model as well as 10+ live project altogether in live instructor-led class along with the various mode of support and services and doubt clearing session.

Course highlights

Syllabus

  1. Introduction to Data Science with Python
  2. Introduction of Python Core
  3. String Objects and Collection
  4. Tuples, Set, Dictionaries & Functions
  5. Map, Reduce & filter functions
  1. OOPS concepts & Exceptional Handling (WEEK 2)
  2. Concept of libraries and modules
  3. Web Scraping & Flask API and mongo DB database
  4. Introduction of data science libraries
  1. Python Pandas
  2. Data Manipulation : Cleansing - Munging Using Python Modules
  1. Data Analysis : Visualization Using Python
  2. Univariate Analysis (Distribution of data & Graphical Analysis)
  3. Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  4. mportant Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
  5. Introduction To Statistics (
  1. Introduction To Predictive Modeling
  2. Concept of model in analytics and how it is used?
  3. Common terminology used in Analytics & Modeling process
  4. Popular Modeling algorithms
  5. Types of Business problems - Mapping of Techniques
  6. Different Phases of Predictive Modeling
  1. Data Exploration For Modeling
  2. Need for structured exploratory data
  3. EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  4. Identify missing data
  5. Identify outliers data
  6. Visualize the data trends and patterns
  7. Need of Data preparation
  8. Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  9. Variable Reduction Techniques - Factor & PCA Analysis
  1. Machine learning -1
  2. Supervised, Unsupervised, Semi-supervised & Reinforcement
  3. Train, Test & Validation splits
  4. Performance, OverFitting & UnderFitting
  5. Linear regression
  6. Assumptions
  7. R-square & adjusted R-square
  8. Intro to Scikit learn
  9. Training methodology
  10. Hands on linear regression
  11. Logistics regression, Precision Recall, Confusion matrix
  12. Sensitivity, Specificity, ROC-Curve
  13. F-score
  14. Decision tree
  15. Cross validation
  16. Bias vs variance
  17. Ensemble approach
  1. XGBoost
  2. Hands on XGBoost
  3. Hyper parameter optimization
  4. Random search cv
  5. Grid search cv
  6. Knearest neighbour
  7. Lazy learners
  8. Curse on dimensionality
  9. KNN issues
  10. Hierarchial Clustering
  11. K-Means
  12. Performance Measurement
  13. Principal component Analysis
  1. SVR
  2. SVM
  3. Naïve Bayes
  4. Polynomial Regression
  5. Ada Boost
  6. Gradient Boost
  7. Isolation Forest
  1. Natural Language Processing
  2. NLP with python
  3. Sentiment analysis
  4. Bags of words
  5. Stemming
  6. Tokenization
  7. Textblob
  1. Time Series Forecasting : Solving Forecasting Problems
  2. Introduction - Applications
  3. Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  4. Classification of Techniques(Pattern based - Pattern less)
  5. Basic Techniques - Averages, Smoothening, etc
  6. Advanced Techniques - AR Models, ARIMA, etc
  7. Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
  8. Model Deployment
  9. Flask Introduction
  10. Flask Application
  11. Deployment on cloud( Heroku & AWS)
  1. Projects
  2. Managing credit card Risks
  3. Bank Loan default classification
  4. Youtube Viewers prediction
  5. Super store Analytics (E-commerce)
  6. Buying and selling cars prediction(like OLX process)
  7. Advanced House price prediction
  8. Analytics on HR decisions
  9. Fake news classifier
  10. Twitter Analysis
  11. Flight price prediction
  1. What is AWS ?
  2. AWS Global Infrastructure
  3. Interacting with AWS
  4. Create an AWS Account
  5. Security and the AWS Shared Responsibility Model
  6. Following IAM Best Practices
  7. AWS Global Infrastructure
  8. Interacting with AWS
  9. Security and the AWS Shared Responsibility Model
  10. Hosting the Employee Directory Application on AWS
  1. Introduction to Serverless Computing with AWS Lambda Part 1
  2. Introduction to Serverless Computing with AWS Lambda Part 2
  3. AWS Lambda Demo Part 1
  4. AWS Lambda Demo Part 2
  5. Introduction to Amazon DynamoDB Part 1
  6. Introduction to Amazon DynamoDB Part 2
  7. Extending Our App Part 1
  8. Extending Our App Part 2
  1. Welcome to AWS Fundamentals: Migrating to the Cloud
  2. Migration Preparation and Business Planning (Phase 1)
  3. Portfolio Discovery and Planning (Phase 2)
  4. Design, Migration and Application Validation (Phase 3 & 4)
  5. Cloud Adoption Framework - Hybrid Environments
  6. Considerations with Migrating DB vs Applications
  7. AWS Server Migration Services
  8. VM Import and VM on AWS (Server Migration Service)
  9. Introduce AWS Migration Hub
  10. AWS Application Discovery Service
  1. Amazon Lex
  2. Creating a Serverless Website with Amazon S3
  3. Introduction to Amazon CloudFront
  4. Introduction to Amazon API Gateway and Demo
  5. Introduction to Serverless Computing with AWS Lambda Part

Features available in our live classes

“who knows, does it live”

Certificate of completion

Life time study material access

Doubt solving at any time

Job opportunities

Instructor

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Python for Bioinformatics course

Our values lie in our instructors

Anand Kumar
Python for Bioinformatics, Vaccine designing

Anand is the Co-Founder and CTO of ReadMyCourse. He has 5 years of experience in working with computation biology, Machine Learning, and Vaccine designing. He has co-authored various research papers in reputed journals and has advanced the career of thousands of students.

Dr. Dibyabhaba Pradhan
NGS data analysis, Vaccine designing, Bioanalyst

Dr. Dibyabhaba Pradhan is a Post-doctoral Research Scientist. He is a PhD in Bioinformatics and has more than 12 years of Research and teaching experience in High throughput NGS data analysis, Computer-aided vaccine design, Rational Drug Design and Medical Informatics. He co-authored more than 53 research papers in International and National Journals of repute.

Nirupma Singh
Python, R, Data Analysis, Machine Learning

Nirupma has 5 years of experience with R and Python programming languages for handling and analysing biological data and implement machine learning. She has a post-graduate in Microbiology therefore, She can understand and interpret the biological data quite well and could provide valuable insights. She enjoys interactive teaching with the learners and give her best to it.

Sharon Priya Alexander
Drug designing, Bioinformatics tools

Sharon Priya Alexander has Masters Degree in Bioinformatics bagged late Dr. P. Subramanyam IAS Gold Medal for being a University topper. He has co-authored various research papers in reputed journals. Currently Pursuing PhD in drug designing.

FAQs

If you miss the class you can attent in any other session. You can view recorded sessions also.

Yes you can attend demo session for free. Before the final class will be started you can attend demo live classes.

Yes you are eligible for refund in the period of three classes.

You will get the certificate after completion of all the live sessions and study materials.

Data Science with cloud computing on AWS