Data Science Course Content (R, Python, ML)
Data Scientists make sense of data that all around us; learn Data science can help make you get the informed decisions to create visualizations.
Data science try to predict future events by Machine learning.
If you are interested in what you are going to learn about the worlds using the data produced every day, then Data Science is for you to get knowledge
Hope InfoTech is a Specialization in series of courses that helps you in master a skill,
To start and Enrol now to Directly Specialization in Data Science to become Data Scientists, visit learner dashboard to take your free Demo with 3 classes, Start Now to Enrol in the contact form.
In this course you will get a Professional Certificate program in Data Science, we cover several Important steps of Data science.
This process like importing data into R, string processing, HTML, works with Dates and time, and text also
Getting started with Data Science and Recommender Systems
Introduction to Data Science, importance of Data Science, statistical and analytical methods,
Deploying Data Science for Business Intelligence, transforming data, machine learning, and introduction to Recommender systems.
Reasons to Use Data Science – Project Life cycle
How Data Science solves real-world problems, Data Science Project Life Cycle, principles of Data Science, introduction to various BI and Analytical tools, data collection.
Introduction to statistical packages, data visualization tools, R Programming, predictive modeling, machine learning, artificial intelligence, and statistical analysis.
- Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data
- Terms of Statistics
- Descriptive statistics, Let us understand some terms in statistics, Variable
- Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots
- Set & rules of probability, Bayes Theorem
- What is probability?, Set & rules of probability, Bayes Theorem
- Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution
- Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling
- Tables & Analysis
- Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables
- Acquiring Data
- Boxplot in R programming, understanding distribution and percentile, identifying outliers, Rstudio Tool, various types of distribution like Normal, Uniform and Skewed
Introduction to R
R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions,
learning to deploy them in various scenarios, use SQL to apply the ‘join’ function, components of R Studio like code editor, visualization, and debugging tools, learn about R-bind.
R-Packages R Functions, code compilation, and data in a well-defined format called R-Packages learn about R-Package structure, Package
metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation, and variables values assignment.
- R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.
Matrices and Vectors
Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.
Reading data from external files
Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.
Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.
Analysis of Variance (ANOVA)
Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.
K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, a cohesive subset of items, solving clustering issues, working with large datasets.
Association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.
Association Rule Mining
Introduction to Association Rule Mining, the various concepts of Association Rule Mining,
Various methods to predict relations between variables in large datasets, the algorithm, and rules of Association Rule Mining, understanding single cardinality.
Regression in R
- Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.
- Analyzing Relationship with Regression
- Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit
- Advance Regression
- Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.
- Logistic Regression
- Logistic Regression Mean, Logistic Regression in R.
- Advance Logistic Regression
- Advanced logistic regression, understanding how to do prediction using logistic regression
- ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.
Receiver Operating Characteristic ( ROC )
Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearity.
How two or more variables are highly correlated, the building of the model, advanced data set partitioning, interpreting of the output.
Predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.
Kolmogorov Smirnov Chart
Data analysis with R, understanding the WALD test, MC Fadden’s pseudo-R-squared,
The significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is a non-parametric test of a one-dimensional probability distribution.
Database connectivity with R
Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.
Integrating R with Hadoop
Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated.
Programming Environment, R programming for MapReduce jobs, and Hadoop execution.
Python Programming Course Content
- Learn the Basics
- Hello, World!, Variables and Types, Lists, Basic Operators, String Formatting, Basic String Operations, Conditions, Loops, Functions, Classes and Objects, Dictionaries, Modules and Packages
- Data Science Tutorials
- Numpy Arrays, Pandas Basics
- Advanced Tutorials
- Generators, List Comprehensions, Multiple Function Arguments, Regular Expressions, Exception Handling, Sets, Serialization, Partial functions, Code Introspection, Closures, Decorators
Machine Learning in Data Science
Deploying machine learning for data analysis, solving business problems, using algorithms for searching patterns in data.
The relationship between variables, multivariate analysis, interpreting correlation, negative correlation.
Deep dive into Data Transformation
Data Transformation key phases Data Mapping and Code Generation, Data Processing operation, data patterns, data sampling.
Sampling distribution, normal and continuous variable, data extrapolation, regression, linear regression model.
Data Testing and Assessment
Data analysis, hypothesis testing, simple linear regression, Chi-square for assessing compatibility between theoretical and observed data.
Implementing data testing on data warehouse, validating data, checking for accuracy, data operational monitoring capabilities.
Data Model, Algorithms & Prediction
Various techniques of data modeling and generating algorithms, methods of business prediction, prediction approaches.
Data sampling, disproportionate sampling, data modeling rules, data iteration, and deploying data for mission-critical applications.
Data Segmentation and Analysis
Working with large data sets in data warehouses, data clustering, grouping, horizontal & vertical slicing, data sharing in partitioning.
Clustering algorithms, K-means Clustering for analysis and data mining, exclusive clustering, hierarchy clustering.
Mahout Clustering algorithm and Probabilistic Clustering,
Nearest neighbor search, pattern recognition, and statistical classification.