## 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

##### 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

ul class=”tutor-course-target-audience-items”>- 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

###### 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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