“The goal is to turn data into information, and information into insight.” – Carly Fiorina
Right decision making has become critical for maintaining competitive advantage in today’s world. Businesses have begun to acknowledge the need to analyze available in-house data and continue to capture and analyze outside organization data to keep moving ahead. Whoever the decision makers, whether mid-level or leaders, possessing the right skills of analysis is imperative to make minimum errors in decision making.
This Data Analytics training empowers your employees and decision makers by upgrading their skills sets and introducing them to appropriate tools to analyze data for day-to-day decision making.
- Do you want your team to acknowledge the importance of Data & thereafter its analysis?
- Are you planning to build analytical capability using Excel’s analytics?
- Do you expect your team to understand basic statistical applications for day to day decision making?
- Do you want to build capability within the team to use R, Python & Tableau technologies?
- Do your non-coding background team members feel that Data Analysis is not their cup of tea?
- Is there any difficulty in creating “Analytics culture” in your organization?
- Would you like to prepare your organization for next level of Analytical capabilities?
- Would you like to scale up your team to automate routine work and shift focus on thinking and planning?
If your answer to these questions is “Yes”, then Data Analytics – Using R, Python, Tableau & Excel is just for you.
- Relate with the importance of Data Analysis
- Utilize and practice tools like R, Python, Tableau & Excel
- Analyze data using Exploratory Data Analysis technique
- Interpret Graphical Presentation of Data
- Apply various Statistical Models
- Examine and use Machine Learning techniques
- Develop ability to use Artificial Intelligence at the next level
The program will focus on hands-on training with data sets using latest & popular technologies like R, Python & Tableau for making participants comfortable with Data Analytics tools and techniques. Post attending the program, participants will be able to perform their day to day work with a scientific approach.
Data Analytics – Using R, Python, Tableau & Excel is an experiential training that addresses a wide range of topics from Exploratory Data Analysis to Deep Learning, concepts of Predictive Analysis, Classifications, Segmentation & Text Mining in between. The training uses data sets for demo and practice and involves live Q&A with the facilitator who comes with over 1500 hours of training experience.
|Session Name||Brief Overview|
|Introduction to Data Science and Business Analytics||Introduction to Data Science and Business Analytics followed by R Installation then R Studio Installation, Brief about R, Comparison with other popular languages like Python, SaS etc.|
|Excel in Data Analytics||Overview of Excel in Data Analysis. Analysis Tool Pack. Graphical Representation. Overview of Tableau, Rattle & Orange for quick and smarter results.|
|Using R for Data Analysis||R for Mathematical and Logical arguments, Types of Variables and How to change them? Why it’s necessary? Vectors, Accessing Vectors, Matrices, Matrix Access. (Instead of R, can first start with excel followed by Python, R and Tableau)|
|Statistics||Standard Error of Mean, Normal Distribution and its application in inferential statistics. Handling missing values and outliers.|
|Graphical Representation of Data||Graphical Representation of data - Histogram, Boxplot, Correlation Plot, Heat Map|
|Data Handling||Learn about Data Wrangling and Data Manipulation techniques, XOR application in R|
|Hypothesis Testing||One Sample T Test. Confidence Level. Level of Significance and P-value creation.|
|Other Statistical Techniques||Paired Sample T Test, Independent Sample T Test, ANOVA, Chi-Square Test. One Sample Proportion Test, Two Sample Proportion Test, Predictive Models, Simple Linear Regression, Multiple Regression, R Square, Multicollinearity, Variance Inflation Factor|
|Classification Methods||Logistic Regression. Binary Classification. Odds Vs Probabilities. Logit Function. Cut-offs. Classification Matrix. ROC Curves. Nagelkerke R Square and applying these on credit data set.|
|Decision Trees||How Partitioning variable and partitioning value is decided? Gini Index & Entropy Criteria for Classification. MSE Criterion for Prediction.|
|Python Training||Know your data in Python. Hypothesis Testing in Python and comparison with R. Support Vector Machine in R. Hyper Planes. Margin. Kernel Trick. Classification Matrix and ROC Curve|
|Text Mining||Text Mining in R. Importing text document. To Lower Case. Removing Punctuations, Digits, Stop Words, Single Letters & White Spaces. Word Clouds. Sentiment Analysis. Three Sentiment Dictionaries. Tidying the documents. Tokenization. Term Frequency. N-grams. Counting & Correlation among Sections. This is followed by Time Series Analysis.|
|Neural Networks and Deep Learning||The Neuron. Feed-Forward Neural Networks. Activation Functions. Sigmoid, Tanh & ReLU Neurons. Softmax Output Layers. Training Feed Forward Neural Networks. Gradient Descent, The Delta Rule and Learning Rates. NN for Prediction and Classification. Deep Learning Basics. Keras. TensorFlow. Theano. CNTK. Installing Keras on CPU. A Binary Classification Example.|