Introduction

1- Introduction 2- Import and export data sets and create data frames within R and Python 3- Sort, merge, aggregate and append data sets. 4- Use measures of central tendency to summarize data and assess symmetry and variation. 5- Differentiate between variable types and measurement scales. 6- Calculate appropriate measures of central tendency based on variable type. 7- Compare variation in two datasets using coefficient of variation. 8- Assess symmetry of data using measures of skewness. 9- Present and summarize distributions of data and relationships between variables graphically. 10- Select appropriate graph to present data 11- Assess distribution using Box-Plot and Histogram. 12- Visualize bivariate relationships using scatter-plots. 13- Present time-series data using motion charts. 14- Introduction 15- Statistical Distributions: Evaluate and analyze standard discrete and continuous distributions, calculate probabilities, and fit distributions to observed. 16- Hypothesis Testing: Formulate research hypotheses, assess appropriate statistical tests, and perform hypothesis testing using R and Python programs. 17- ANOVA/ANCOVA: Analyze the concept of variance, define variables and factors, evaluate sources of variation, and perform analysis using R and Python. 18- Introduction 19- Fundamentals of Predictive Modelling. 20- Carry out parameter testing and evaluation. 21- Validate assumptions in multiple linear regression. 22- Validate models via data partitioning and cross-validation. 23- Introduction 24- Time Series Analysis: Learn concepts, stationarity, ARIMA models, and panel data regression. 25- Introduction 26- Unsupervised Multivariate Methods. 27- Principal Component Analysis (PCA) and its derivations. 28- Hierarchical and non-hierarchical cluster analysis. 29- Panel data regression. 30- Data reduction. 31- Scoring models 32- Multi-collinearity resolution 33- Brand perception mapping 34- Cluster solution interpretation 35- Use of clusters for business strategies 36- Introduction 37- Advance Predictive Modeling 38- Evaluating when to use binary logistic regression correctly. 39- Developing realistic models using functions in R and Python. 40- Interpreting output of global testing using linear regression testing to assess results. 41- Performing out of sample validation to test predictive quality of the model Developing applications of multinomial logistic regression and ordinal. 42- Selecting the appropriate method for modeling categorical variables. 43- Developing models for nominal and ordinal scaled dependent variables in R and Python correctly Developing generalized linear models . 44- Evaluating the concept of generalized linear models. 45- Applying the Poisson regression model and negative binomial regression to count data correctly. 46- Modeling 'time to event' variables using Cox regression. 47- Introduction 48- Classification methods: Evaluate different methods of classification and their performance in order to design optimum classification rules. 49- Naïve Bayes: Understand and appraise the Naïve Bayes classification method. 50- Support Vector Machine algorithm: Understand and appraise the Support Vector Machine algorithm for classification. 51- Decision tree and random forest algorithms: Apply decision trees and random forest algorithms to classification and regression problems. 52- Bootstrapping and bagging: Analyze the concepts of bootstrapping and bagging in the context of decision trees and random forest algorithms. 53- Market Baskets: Analyze transaction data to identify possible associations and derive baskets of associated products. 54- Neural networks: Apply neural networks to classification problems in domains such as speech recognition, image recognition, and document categorization. 55- Introduction 56- Text mining: Concepts and techniques used in analyzing unstructured data. 57- Sentiment analysis: Identifying positive, negative, or neutral tone in Twitter data. 58- SHINY package: Building interpretable dashboards and hosting standalone applications for data analysis. 59- Hadoop framework: Core concepts and applications in Big Data Analytics. 60- Artificial intelligence: Building simple AI models using machine learning algorithms for business analysis. 61- SQL programming: Core SQL for data analytics and uncovering insights in underutilized data. 62- Introduction 63- Transformation and key technologies: Analyze technologies driving digital transformation and assess the challenges of implementing it successfully. 64- Strategic impact of Big Data and Artificial Intelligence: Evaluate theories of strategy and their application to the digital economy, and analyze. 65- Theories of innovation: Appraise theories of disruptive and incremental change and evaluate the challenges of promoting and implementing innovation. 66- Ethics practices and Data Science: Assess the role of codes of ethics in organizations and evaluate the importance of reporting. 67- Handle and manage multiple datasets within R and Python environments.