Data Analytics

Tools to Master: Excel, Python, SQL, NoSQL, Power BI, Presto, Knime

Skills to Master: SQL, Data Wrangling, Prediction Algorithms, Data Visualization Using Power BI, Time Series, Machine Learning, Power BI, Advanced Statistics, Data Minin

• Introduction to Data Science
• Create and Modify Tables
• Sort and Filter Data

• Visualize Data with Charts
• Modify and Format Charts
• Apply Best Practices in Chart Design

• Analyze Data with Formulas and Named Ranges
• Analyze Data with Functions
• Implement Data Validation, Forms, and Controls
• Create Conditional Visualizations with Lookup Functions

• Create a PivotTable
• Analyze PivotTable Data

• Visualize Data with PivotCharts
• Filter Data Using Slicers and Timelines
• Create a Dashboard in Excel

• Create Map Charts in Excel
• Customize Map Charts in Excel

• Connect to Data with Queries
• Clean and Combine Data
• Shape and Transform Data

• Anaconda Jupyter Environment Setup.
• Python language Overview.
• Data types of Python, numbers, string.
• If, el-if, Loops in python.
• Functions and modules in python
• Lambda function.
• Strings methods.
• List and its methods.
• Tuple, set, dictionary and their methods

• Understanding the uses of various open source libraries.
• Importing various modules with different methods.
• Working with Numpy.
• Numerical operations on Numpy array.
• Exploring various use cases of Numpy.
• Financial Analysis using scikit-learn, QuantLib, SciPy

• Matplotlib, Seaborne, Plotly and Cufflinks.
• Draw different types of graphs using above modules.
• Pie chart, histogram, bar chart, boxplot, count plot etc.

• Dash Board Preparation with BI.
• Connect to Kaggle Datasets.
• Explore Pandas Data Frame.
• Analyze and manipulate Pandas Data Frame.
• Data cleaning with Python & Export to BI.
• Data Visualization with Python.
• Connect to web data with Power BI.
• Clean and transform web data with Power BI.
• Create data visualization with Power BI.
• Publish reports to Power BI Service.
• Transform less structured data with Power BI.
• Connect to data source with excel.
• Prep query with excel Power query.
• Data cleaning with excel.
• Create data model and build relationships.
• Analyze data with Pivot Tables
• Analyze data with Pivot Charts
• Connect to data sources with Power BI
• Join related data and create relationships with PowerBI

• Introduction to Presto
• Writing Queries in Presto on large data sets.
• Data Transformation using Presto

• Introduction to SQL
• SQL operators
• Join, tables, and variables
• SQL functions
• Subqueries
• SQL functions, views, and stored procedures
• User-defined functions
• SQL performance and optimization
• Advanced concepts
• Cloud Operation Using Fire Base
• No SQL Schema Using Fire Base
• Fire Base Migration With Python

• Introduction to KNIME
• Working with data in KNIME
• Loops in KNiME
• Webscraping in KNIME
• Hyperparameter optimization in KNIME
• Hyperparameter optimization for Machine Learning Models using loops in KNIME
• Feature Selection in KNIME

• Multiple linear regression
• Logistic regression
• Linear discriminant analysis

• Introduction to time-series
• Correlation
• Forecasting
• Autoregressive models

• Programming with Python
• Advance Statistics
  • ANOVA
  • Regression analysis
• Data Mining
  • Supervised and unsupervised learning
  • Clustering
  • Decision trees
  • Neural networks