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Data Analytics

Course Overview

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

Lesson 1: Data Analysis Fundamentals

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

Lesson 2: Visualizing Data With Excel

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

Lesson 3: Analyzing Data With Formulas And Functions

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

Lesson 4: Analyzing Data With Pivottables

  • Create a PivotTable
  • Analyze PivotTable Data

Lesson 5: Presenting Visual Insights With Dashboards In Excel

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

Lesson 6: Creating Geospatial Visualizations With Excel

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

Lesson 7: Getting And Transforming 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

Lesson 9: Analysis Using Python

  • 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

Lesson 10: Data Visualization Using Python

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

Lesson 11: Data Analytics Using Power Bi

  • 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

Lesson 12: Presto

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

Lesson 13: Data Wrangling With Sql/No Sql

  • 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

Lesson 14: Knime

  • 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

Lesson 15: Predictive Modeling

  • Multiple linear regression
  • Logistic regression
  • Linear discriminant analysis

Lesson 16: Time-Series Forecasting

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

Lesson 17: Statistics & Machine Learning Using Python

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

Notes:

* Course topics and duration may be modified by the instructor based upon the knowledge and skill level of the course participants.

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