Data Visualization and Analytics syllabus

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Introduction to Data Science and Big Data, Applications of Data Science, 5 V’s of Big Data, Relationship between Data Science and Information Science, Business intelligence versus Data Science, Data: Data Types, Data Collection.
• Need of Data wrangling, Methods: Data Cleaning, Data Integration, Data Reduction, Data Transformation, Data Discretization.
• Data Analytic Life cycle: Introduction, Phase 1: Discovery, Phase 2: Data Preparation, Phase 3: Model Planning, Phase 4: Model Building, Phase 5: Communication results, Phase 6: Operationalize.
• Case study: Global Innovation Social Network and Analysis (GINA).

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Statistical concepts, Measures of Central Tendency: Mean, Median, Mode, Mid-range.
• Measures of Dispersion: Range, Variance, Mean Deviation, Standard Deviation.
• Bayes theorem, Basics and need of hypothesis and hypothesis testing, Pearson Correlation, Sample Hypothesis testing, Chi-Square Tests, t-test.
• Case Study: For an employee dataset, create measure of central tendency and its measure of dispersion for statistical analysis of given data.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Essential Python Libraries, Basic examples.
• Data Preprocessing: Removing Duplicates, Transformation of Data using function or mapping, replacing values, Handling Missing Data.
• Data Analytics Types: Predictive, Descriptive and Prescriptive.
• Association Rules: Apriori Algorithm Regression: Linear Regression, Logistic Regression.
• Classification: Decision Trees.
• Introduction to Scikit-learn: Installations, Dataset, Regression and Classification using Scikit-learn.
• Clustering Algorithms: K-Means.
• Case Study: Use Spam mail dataset and apply data preprocessing methods.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• History of text mining, Roots of text mining overview of seven practices of text analytic, Application.
• Introduction to Text Analysis: Text-preprocessing, Bag of words, TF-IDF and topics.
• Need and Introduction to social network analysis.
• Naïve Bayes classifier for text classification, Model Evaluation and Selection: Metrics for Evaluating Classifier Performance, Holdout Method and Random Sub sampling, Parameter Tuning and Optimization, Confusion matrix, AUC-ROC Curves.
• Case Study: Use Spam mail dataset and apply Naïve Bayes classifier and evaluate model.

Derived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.

• Introduction to Data visualization, Challenges to Big data visualization, Data Visualization using Python: Line plot, Scatter plot, Histogram, Density plot, Box-plot, Pie Chart, Area Plot, Heatmap, Funnel Chart.
• Open source data visualization tools, Data Visualization using Tableau.
• Hadoop Ecosystem and Technologies, Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell commands, Anatomy of File Write and Read, NameNode, Secondary NameNode, and DataNode, Hadoop MapReduce paradigm, Map Reduce tasks, Job, Task trackers, Apache SPARK Framework, Difference between Hadoop and Apache Spark.
• Case Study: Sales Data Visualization using Tableau.