- 60 hours of educator driven Training
- Customer Lifecycle Management
- Social Media Behavior & Link Analysis
- Genetic Research
- Product Analysis
- 1 month Intensive Training
- Customer Service
- Fraud Detection
- Inventory Management
- Target Marketing
- Business Management & Engineering Student
- Mathematics, Statics and Economics students
This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data.
Get a fair and practical knowledge of statistical & quantitative analysis, explanatory & predictive modelling and fact-based analysis to drive decision making. Enhance your data interpretation & problem-solving at work. Get exposed to the basics of data management, decision trees, logistic regression, segmentation, design of experiments, and forecasting with R, SAS and Microsoft Excel Tools- best for all kind of business analyst jobs. Staying abreast of the latest new forecasts can spark innovative ideas, bringing more depth to a company’s brand. Improving processes paves the way to releasing innovative new products, services and information. And that can help a company charge ahead of its competition.
Module 1: Statistics and Data Science
Introduction to Statistics and Data Science and its Life Cycle
Statistical inference and modelling are indispensable for analyzing data affected by chance, and thus essential for data scientists. This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we’ll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well, also provide an estimate of the precision of your forecast.
Module 2: Basic of R and Data Analysis
Basic of R and Data Analysis, Data Cleaning and preparing data for analysis in R Language
The open-source programming language R has for a long time been popular (particularly in academia) for data processing and statistical analysis. Among R’s strengths are that it’s a succinct programming language and has an extensive repository of third party libraries for performing all kinds of analyses. Read data from flat files into R’s data frame object, investigate the structure of the dataset and make corrections, and store prepared datasets for later use. Prepare and transform the data. Calculate essential summary statistics, do crosstabulation, write your own summary functions, and visualize data with the ggplot2 package. Build predictive models, evaluate and compare models, and generate predictions on new data.
Module 3: CLT, Different Types of Test, HT and Tests
Measures and spread, CLT, Different Types of Test, HT and Tests, Bivariate Analysis, ANOVA
Analysis of variance (ANOVA) is a collection of statistical models and their associated procedures (such as “variation” among and between groups) used to analyse the differences among group means. ANOVA was developed by statistician and evolutionary biologist Ronald Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether the means of several groups are equal, and therefore generalizes the t-test to more than two groups. ANOVA is useful for comparing (testing) three or more means (groups or variables) for statistical significance. It is conceptually similar to multiple two-sample t-tests, but is more conservative (results in less type I error) and is therefore suited to a wide range of practical problems.
Module 4: Linear Regression
Introduction to Linear Regression
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
Module 5: Logistic Regression
Introduction to Logistic Regression
Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables
Module 6: Supervised & Unsupervised Algorithm
Introduction to Supervised & Unsupervised Algorithm
The majority of practical machine learning uses supervised learning. Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
Module 7: Market Basket Analysis
Introduction to Market Basket Analysis
Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.
Module 8: Time Series Modeling
Introduction to Time Series Modeling
A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, atime series is a sequence taken at successive equally spaced points in time. … Time seriesforecasting is the use of a model to predict future values based on previously observed values.
Module 9: Ridge Regression
Introduction to Ridge Regression
Regression analysis is used in stats to find trends in data. For example, you might guess that there’s a connection between how much you eat and how much you weigh; regression analysis can help you quantify that. Regression analysis will provide you with an equation for a graph so that you can make predictions about your data.
Module 10: Neural Network
Introduction to Neural Network
A neural network is a powerful computational data model that is able to capture and represent complex input/output relationships. …Neural networks resemble the human brain in the following two ways: A neural network acquires knowledge through learning.
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