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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
      • Bias/Variance Tradeoff
      • 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
      • 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)
      • Creating an Array
      • Indexing an Array
      • Operations between 2 Arrays
      • Operations between an Array and a Vector
      • Outer Products
      • A Matrix is a 2-Dimensional Array
      • Creating a Matrix
      • Matrix Multiplication
      • Merging Matrices
      • Solving a set of linear equations
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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
      • Reading Data from files
      • 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

      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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