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# Data Science Course Syllabus

## DataScience Course syllabus

Gain expertise in Data Analytics through our well-framed Data Science Course Syllabus. We will cover in-demand industry skills such as basic data science concepts, statistics, probability, Python review, predictive model, machine learning process, filtering, data mining, python for machine learning, dealing with real-time data, Spark fundamentals, Big data basics, A/B testing, deep learning, natural networks, R basics, descriptive statistics, inferential statistics, vectors, arrays, matrices, factors, linear regression, and data visualization in our Data Science Course Curriculum at SLA.

### Getting Started

• Course Introduction
• Course Material & Lab Setup
• Installation
• Python Basic – Part – 1
• Python Basic – Part – 2
• Advance Python – Part – 1
• Advance Python – Part – 2

#### Statistics and Probability Refresher, and Python Practice

• Types of Data
• Mean, Median, Mode
• Using mean, median, and mode in Python
• Variation and Standard Deviation
• Probability Density Function; Probability Mass Function
• Common Data Distributions
• Percentiles and Moments
• A Crash Course in matplotlib
• Covariance and Correlation
• Conditional Probability
• Exercise Solution: Conditional Probability of Purchase by Age
• Bayes’ Theorem
##### Predictive Models
• Linear Regression
• Polynomial Regression
• Multivariate Regression, and Predicting Car Prices
• Multi-Level Models
##### Machine Learning with Python
• Supervised vs. Unsupervised Learning, and Train/Test
• Using Train/Test to Prevent Overfitting a Polynomial Regression
• Bayesian Methods: Concepts
• Implementing a Spam Classifier with Naive Bayes
• K-Means Clustering
• Clustering people based on income and age
• Measuring Entropy
• Install GraphViz32. Decision Trees: Concepts
• Decision Trees: Predicting Hiring Decisions
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• Using SVM to cluster people using scikit-learn
##### Recommender Systems
• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• Finding Movie Similarities
• Improving the Results of Movie Similarities
• Making Movie Recommendations to People
• Improve the recommender’s results
##### More Data Mining and Machine Learning Techniques
• K-Nearest-Neighbors: Concepts
• Using KNN to predict a rating for a movie
• Dimensionality Reduction; Principal Component Analysis
• PCA Example with the Iris data set
• Data Warehousing Overview: ETL and ELT
• Reinforcement Learning
##### Dealing with Real-World Data
• K-Fold Cross-Validation to avoid overfitting
• Data Cleaning and Normalization
• Cleaning web log data
• Normalizing numerical data
• Detecting outliers
##### Apache Spark: Machine Learning on Big Data
• Lab Set-up Warning & Error Handling
• Installing Spark – Part – 1
• Installing Spark – Part – 2
• Spark Introduction
• Spark and the Resilient Distributed Dataset (RDD)
• Introducing MLLib
• Decision Trees in Spark
• K-Means Clustering in Spark
• TF / IDF
• Searching Wikipedia with Spark
• Using the Spark 2.0 DataFrame API for MLLib
##### Experimental Design
• A/B Testing Concepts
• T-Tests and P-Values
• Hands-on With T-Tests
• Determining How Long to Run an Experiment
• A/B Test Gotchas
##### Deep Learning and Neural Networks
• Deep Learning Pre-Requisites
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow, Part 1
• Using Tensorflow, Part 2
• Introducing Keras
• Using Keras to Predict Political Affiliations
• Convolutional Neural Networks (CNN’s)
• Using CNN’s for handwriting recognition
• Recurrent Neural Networks (RNN’s)
• Using a RNN for sentiment analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning
##### Statistics and Data Science in R – Intro
• Introduction to R
• R and R studio Installation & Lab Setup
• Descriptive Statistics
##### Descriptive Statistics
• Mean, Median, Mode
• Our first foray into R : Frequency Distributions
• Draw your first plot : A Histogram
• Computing Mean, Median, Mode in R
• What is IQR (Inter-quartile Range)?
• Box and Whisker Plots
• The Standard Deviation
• Computing IQR and Standard Deviation in R
##### Inferential Statistics
• Drawing inferences from data
• Random Variables are ubiquitous
• The Normal Probability Distribution
• Sampling is like fishing
• Sample Statistics and Sampling Distributions
##### Case studies in Inferential Statistics
• Case Study 1 : Football Players (Estimating Population Mean from a Sample)
• Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
• Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
• Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
• Case Study 5: A/B Testing (Comparing the means of two populations)
• Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)
##### Diving into R
• Harnessing the power of R
• Assigning Variables
• Printing an output
• Numbers are of type numeric
• Characters and Dates
• Logicals
##### Vectors
• Data Structures are the building blocks of R
• Creating a Vector
• The Mode of a Vector
• Vectors are Atomic
• Doing something with each element of a Vector
• Aggregating Vectors
• Operations between vectors of the same length
• Operations between vectors of different length
• Generating Sequences
• Using conditions with Vectors
• Find the lengths of multiple strings using Vectors
• Generate a complex sequence (using recycling)
• Vector Indexing (using numbers)
• Vector Indexing (using conditions)
• Vector Indexing (using names)
##### Arrays
• Creating an Array
• Indexing an Array
• Operations between 2 Arrays
• Operations between an Array and a Vector
• Outer Products
##### Matrices
• A Matrix is a 2-Dimensional Array
• Creating a Matrix
• Matrix Multiplication
• Merging Matrices
• Solving a set of linear equations
##### Factors
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• What is a factor?
• Find the distinct values in a dataset (using factors)
• Replace the levels of a factor
• Aggregate factors with table()
• Aggregate factors with tapply()
##### Lists and Data Frames
• Introducing Lists
• Introducing Data Frames
• Indexing a Data Frame
• Aggregating and Sorting a Data Frame
• Merging Data Frames
##### Regression quantifies relationships between variables
• Linear Regression in Excel : Preparing the data.
• Linear Regression in Excel : Using LINEST()
##### Linear Regression in R
• Linear Regression in R : Preparing the data
• Linear Regression in R : lm() and summary()
• Multiple Linear Regression
• Adding Categorical Variables to a linear mode
• Robust Regression in R : rlm()
• Parsing Regression Diagnostic Plots
##### Data Visualization in R
• Data Visualization
• The plot() function in R
• Control color palettes with RColorbrewer
• Drawing bar plots
• Drawing a heatmap
• Drawing a Scatterplot Matrix
• Plot a line chart with ggplot
###### Conclusion

Deep dive into the data science ocean with our perfectly designed Data Science Course Syllabus. We at SLA provide the best Data Science Training in Chennai for global aspirants.

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