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

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

    Lesson 8: Revisited to Python & Anaconda Setup

    • 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

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