Advanced Analytics using Excel and Python

2A robust Advanced Analytics Course which will lead you to your Data Analytics Goal and on the path to becoming a Data Scientist with strong foundations.


Irrespective of your educational and professional background this course builds your skills from the ground up. It covers basics of Statistics and Data Analytics and shows you how to leverage Excel and Python and create fantastic visualizations for the data sets and pull out meaningful insights. 
We have special weekend batches running for this course. 


                                    Advanced Analytics Using Excel and Python


  • Mode of Delivery -  (Online) (36 Hours)
  • Evaluation - Presentation (10 Marks) + Project Work (50 Marks) + 4 Regular Assignments (40 Marks)
  • Enrolling for the course - Call +91 -84590 44764, to reserve a seat or Email to info@analyticsdomain.com
  • Course Fee - INR 13,924/- per participant. (Inclusive of all taxes). No cash payment or 3rd Party Payment through individuals is allowed.                 Participants are requested to pay by cheque or account transfer to ANALYTICS DOMAIN, payable at Pune. Mode of Payment: - Drawing a Cheque/DD in favor of “ANALYTICS DOMAIN” payable at Pune. For Account Transfers such as RTGS or NEFT: Current account number 0102102000026707 with IDBI BANK, Hadapsar, Pune; IFSC No. IBKL0000102. Organization Name: ANALYTICS DOMAIN 
  • Documents Necessary - 1. Valid ID Proof scanned copy (Adhar Card / Driving Licence or other document)  2. Latest Resume for those looking for placement / referrals / Internship or Project workt 

                                                      Course Curriculum         

   

Module 1: Introduction to Data Analytics

Use Case of Data Analytics

· Introduction to Data Analytics and its relevance to all Domain areas

· Concepts of Data Analytics and its application areas

· What it takes to become a great Data Analyst

· Clarifications of jargons such as Data Mining, Business Analytics, Functional Analytics, Machine Learning, Artificial Intelligence and Data Science 

· Advanced Excel Labs and Demos, Python Basics


Module 2: Understanding your data through Advanced Analytics

Statistical Concepts

· Concepts of Experiments, Outcomes, Sample space, events and Probability Models in relation to Advanced Analytics

· Concepts of Conditional Probability, Independence and Bayes Theorem

· Concept of lift, Support, sensitivity and specificity and their illustrations in Business Analytics

· Random variables, Data generation process, sample and population, parameters and inferential problems

· Distribution Functions and scripts in Python to support the learning

· Markov Chains and usage 

· Simulation concepts

· Understanding coding in Python Numpy and Advanced Analytics


Module 3: Deep Dive into Data Analytics using Knime

Exploratory Data Analysis with Knime

· Using Knime to summarize data

· Cleaning raw data for modelling

· Reducing dimensions with Principal Component Analysis

· Extending Knime with user–defined workflows

Facilitating good analytical thinking with data visualization

· Investigating characteristics of a data set through visualization

· Charting data distributions with boxplots, histograms and density plots

· Identifying outliers in data and how to tackle them for better for Data Engineering

· Pulling out Insight from Data and presenting the outcome


Module 4: Working with Unstructured Data

Mining unstructured data for business applications

· Preprocessing unstructured data in preparation for deeper analysis

· Describing a corpus of documents with a term–document matrix

· Make predictions from textual data

· Leveraging Knime to create Text Analytic workflows

· Using Python for Text Analytics

· Understanding issues when working with different Lexicons and Languages


Module 5: Predicting Outcomes with Regression Techniques

Estimating future values with linear regression

· Modelling the numeric relationship between an output variable and several input variables

· Correctly interpreting coefficients of continuous data

· Assess your regression models for ‘goodness of fit’ using Knime and Python

· Linear, Multiple and Logistic Regression Analysis and their practical implications using Python and Knime


Module 6: Categorizing Data with Classification Techniques using Python and Knime

Automating the labelling of new data items

· Understanding various Classification Techniques and their usage

· Understanding the underlying algorithms in Classification Techniques (KNN, Naïve Baye’s, SGD, Support Vector Machines and Artificial Neural Networks) 

· Predicting target values using Decision Trees, Random Forests, Ensemble

· Constructing training and test data sets for predictive model building

· Dealing with issues of overfitting

Assessing model performance

· Evaluating classifiers with confusion matrices

· Calculating a model’s error rate


Module 7: Detecting Patterns in Complex Data with Clustering using Python and Knime

Identifying previously unknown groupings within a data set

· Segmenting the customer market with the K–Means algorithm

· Defining similarity with appropriate distance measures

· Constructing tree–like clusters with hierarchical clustering

· Clustering text documents and tweets to aid understanding


Module 8: Data Visualization 

· Business Intelligence Concepts and Graphing Techniques

· Graphing your data through Advanced Excel Analytics

· Using Python Graphic packages to visualize your data

· Knime as a Visualizing tool

· Other tools and packages such as BIRT for Data Visualization


(Technical Skills normally picked up by trainees during the Training)

Advanced Statistics

Python and its libraries like - Numpy, Pandas, Matplotlib, Scipy, Plotly, Bokeh etc., 

Advanced Excel

Data Visualization Tools and Reporting tools such as Qlikview and Data Studio

Knime

Predictive Analytics tools

Text Analytics Tools

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