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Data Science Authority follows a learning map approach to teach various concepts in data science.
Exploratory Data Analysis | Statistical Modeling | Big Data Analytics | Text Analytics | Artificial Intelligence
Introduction to Data Science
History and Evolution of Data Science
A typical day in a Data Scientist’s life
Trends in Data Science
Data science learning curve
Applications of Data science
Advanced Visualizations
Basics of Visualization
Rules of Visual Perception
Tufte’s rules Visualizing
Multiple dimensions
Introduction to Tableau
Art of Story Telling
Regression Analysis
Correlation Analysis
Simple linear Regression
Logistic Regression
Dummy Coding
Interaction Variables
Diagnostic techniques
Big Data
Structured Vs. Unstructured data
4 Vs of Big Data
Applications of Big Data
Introduction to Map-Reduce
Hadoop Ecosystem (Pig, Hive, Flume, Zookeeper etc.) Introduction to Spark and Scala
Text Analytics
Introduction to Text Analytics
Twitter connection
Web scraping –Amazon.com
Introduction to NLTK
Creation of word cloud
Lexicon Based Analysis
Sentiment Analysis
Topic Mining
Entity Extraction
Machine Learning-IV
R-Spark ML
Introduction to Recurrent Neural Networks
Introduction to Reinforcement Learning
Business Fundamentals
Understanding of Business Imperatives
Business and Process Outcomes
DSA’s Pinch to zoom framework
DSA’s Lenz framework
Freeze Cream Game
Business Statistics-I
Introduction to Probability
Descriptive Statistics
Probability Distributions
Normal Distribution
Central Limit Theorem
Forecasting Techniques
Qualitative Forecasting
Introduction to Time Series Forecasting
Naïve forecasting
Moving Averages forecasting
Exponential Smoothing
AR, MA, ARIMA models
Dealing with Seasonality
Introduction to Python
Say Hello to Python
Meet Pycharm
Introduction to Object Oriented Programming(OOPS)
Numpy
Pandas
Matplotlib
Scikitlearn
Machine Learning-II
Introduction to Unsupervised Learning
K-Nearest Neighbors Model
Hierarchical Clustering
Principal Component Analysis
Artificial Intelligence
Introduction to Deep Learning
Applications of Deep Learning
Deep Learning-CNN
Working with Keras and Tensorflow
Training Deep Learning models on GPUs
Data Preparation
Data Collection
Data Classification
Plotting Data
Data Cleansing
Factor Analysis
Data Imputation
Data Partitioning
Data Transformation
Laws of Dimensionality
Business Statistics-II
Population and Sample estimation
Hypothesis Testing
Types of error- Type-I/Type-II
One tail Test
Two Tail Test
Anova
Machine Learning-I
What is Machine learning?
Decision Tree working model
Pruning Techniques
Introduction to Conditional Probability
Bayesian Analysis
Multinomial Logistic Regression
Random Forest techniques
Association Rule Mining - Apriori Algorithm
Model Validation Techniques
Model Scoring
Best fit Models
Optimization Techniques
Applications of Optimization Techniques
Linear Programming using solver
Concept of Duality Shadow prices and decisions
Introduction to Integer Programming
Machine Learning-III
Introduction to Supervised learning
What is a Perceptron
Multilayered Perceptron
Artificial Neural Networks
Gradient Descent Algorithm
Designing ANN
Secrets of Data Science
Data Science Extended Learning Map
Best Practices
Data Privacy
Job Description analysis for various Data Science profiles
What Next - One-one session with Mentor
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