Data Science Course Syllabus

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Data Science Course Syllabus by the Best Data Science Training Institute in Chennai is thoughtfully designed to meet the current industry demands. To ensure that you have the best learning experience possible, our Data Science Syllabus includes the most recent and up-to-date real-world examples. Our Syllabus of Data Science gives you practical experience to innovative technologies like Machine Learning, Python, R Programming, Tableau, and Big Data. Build a thriving career in the field of Data Science by enrolling in Softlogic’s Data Science Course in Chennai.

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    Java Syllabus

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    Our Data Science Course Syllabus is well-suited for individuals of any level of expertise. This specialized Data Science Course for Beginners will offer you the comprehensive knowledge and skills required in this domain of Data Science. The Data Science Course Content covers extensive topics, ensuring individuals are well-prepared to thrive in the industry. The following is the outline of the Data Science Course Syllabus:

    • Introduction to Data Analytics
    • Introduction to Business Analytics
    • Understanding Business Applications
    • Data types and data Models
    • Type of Business Analytics
    • Evolution of Analytics
    • Data Science Components
    • Data Scientist Skillset
    • Univariate Data Analysis
    • Introduction to Sampling
    • Introduction to R programming
    • Types of Objects in R
    • Naming standards in R
    • Creating Objects in R
    • Data Structure in R
    • Matrix, Data Frame, String, Vectors
    • Understanding Vectors & Data input in R
    • Lists, Data Elements
    • Creating Data Files using R
    • Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
    • Sub-setting Data
    • Selecting (Keeping) Variables
    • Excluding (Dropping) Variables
    • Selecting Observations and Selection using Subset Function
    • Merging Data
    • Sorting Data
    • Adding Rows
    • Visualization using R
    • Data Type Conversion
    • Built-In Numeric Functions
    • Built-In Character Functions
    • User Built Functions
    • Control Structures
    • Loop Functions
    • Basic Statistics
    • Measure of central tendency
    • Types of Distributions
    • Anova
    • F-Test
    • Central Limit Theorem & applications
    • Types of variables
    • Relationships between variables
    • Central Tendency
    • Measures of Central Tendency
    • Kurtosis
    • Skewness
    • Arithmetic Mean / Average
    • Merits & Demerits of Arithmetic Mean
    • Mode, Merits & Demerits of Mode
    • Median, Merits & Demerits of Median
    • Range
    • Concept of Quantiles, Quartiles, percentile
    • Standard Deviation
    • Variance
    • Calculate Variance
    • Covariance
    • Correlation
    • Hypothesis Testing
    • Multiple Linear Regression
    • Logistic Regression
    • Market Basket Analysis
    • Clustering (Hierarchical Clustering & K-means Clustering)
    • Classification (Decision Trees)
    • Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)
    • Standard Normal Distribution
    • Normal Distribution
    • Geometric Distribution
    • Poisson Distribution
    • Binomial Distribution
    • Parameters vs. Statistics
    • Probability Mass Function
    • Random Variable
    • Conditional Probability and Independence
    • Unions and Intersections
    • Finding Probability of dataset
    • Probability Terminology
    • Probability Distributions
    • Bubble Chart
    • Sparklines
    • Waterfall chart
    • Box Plot
    • Line Charts
    • Frequency Chart
    • Bimodal & Multimodal Histograms
    • Histograms
    • Scatter Plot
    • Pie Chart
    • Bar Graph
    • Line Graph
    • Overview & Terminologies
    • What is Machine Learning?
    • Why Learn?
    • When is Learning required?
    • Data Mining
    • Application Areas and Roles
    • Types of Machine Learning
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement learning

    Steps in developing a Machine Learning application

    • Key tasks of Machine Learning
    • Modelling Terminologies
    • Learning a Class from Examples
    • Probability and Inference
    • PAC (Probably Approximately Correct) Learning
    • Noise
    • Noise and Model Complexity
    • Triple Trade-Off
    • Association Rules
    • Association Measures
    • Concept of Regression
    • Best Fitting line
    • Simple Linear Regression
    • Building regression models using excel
    • Coefficient of determination (R- Squared)
    • Multiple Linear Regression
    • Assumptions of Linear Regression
    • Variable transformation
    • Reading coefficients in MLR
    • Multicollinearity
    • VIF
    • Methods of building Linear regression model in R
    • Model validation techniques
    • Cooks Distance
    • Q-Q Plot
    • Durbin- Watson Test
    • Kolmogorov-Smirnof Test
    • Homoskedasticity of error terms
    • Logistic Regression
    • Applications of logistic regression
    • Concept of odds
    • Concept of Odds Ratio
    • Derivation of logistic regression equation
    • Interpretation of logistic regression output
    • Model building for logistic regression
    • Model validations
    • Confusion Matrix
    • Concept of ROC/AOC Curve
    • KS Test
    • Applications of Market Basket Analysis
    • What is association Rules
    • Overview of Apriori algorithm
    • Key terminologies in MBA
    • Support
    • Confidence
    • Lift
    • Model building for MBA
    • Transforming sales data to suit MBA
    • MBA Rule selection
    • Ensemble modelling applications using MBA
    • Model building using ARIMA, ARIMAX, SARIMAX
    • Data De-trending & data differencing
    • KPSS Test
    • Dickey Fuller Test
    • Concept of stationarity
    • Model building using exponential smoothing
    • Model building using simple moving average
    • Time series analysis techniques
    • Components of time series
    • Prerequisites for time series analysis
    • Concept of Time series data
    • Applications of Forecasting
    • Understanding the Concept
    • Internal decision nodes
    • Terminal leaves.
    • Tree induction: Construction of the tree
    • Classification Trees
    • Entropy
    • Selecting Attribute
    • Information Gain
    • Partially learned tree
    • Overfitting
    • Causes for over fitting
    • Overfitting Prevention (Pruning) Methods
    • Reduced Error Pruning
    • Decision trees – Advantages & Drawbacks
    • Ensemble Models
    • Parametric Methods Recap
    • Clustering
    • Direct Clustering Method
    • Mixture densities
    • Classes v/s Clusters
    • Hierarchical Clustering
    • Dendogram interpretation
    • Non-Hierarchical Clustering
    • K-Means
    • Distance Metrics
    • K-Means Algorithm
    • K-Means Objective
    • Color Quantization
    • Vector Quantization
    • Tableau Introduction
    • Data connection to Tableau
    • Calculated fields, hierarchy, parameters, sets, groups in Tableau
    • Various visualizations Techniques in Tableau
    • Map based visualization using Tableau
    • Reference Lines
    • Adding Totals, sub totals, Captions
    • Advanced Formatting Options
    • Using Combined Field
    • Show Filter & Use various filter options
    • Data Sorting
    • Create Combined Field
    • Table Calculations
    • Creating Tableau Dashboard
    • Action Filters
    • Creating Story using Tableau
    • Clustering using Tableau
    • Time series analysis using Tableau
    • Simple Linear Regression using Tableau
    • Integrating R code with Tableau
    • Creating statistical model with dynamic inputs
    • Visualizing R output in Tableau
    • Case Study 1- Real time project with Twitter Data Analytics
    • Case Study 2- Real time project with Google Finance
    • Case Study 3- Real time project with IMDB Website

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    Conclusion

    Underline

    In conclusion, our Data Science Course Syllabus is designed to empower you to utilize the complete potential of data and build a fulfilling and successful career. Level up your Data Science career by enrolling in Softlogic’s Data Science Training in Chennai.

    Contact us via Whatsapp for more info: 86818 84318. Or Grab your free demo class now!

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