Data Science has specific deliverables and goals that come with it. These deliverables help in addressing the goals of solving the problem at hand. Some of them are, Prediction analysis based on the inputs given, Social media recommendations used on YouTube/Netflix, Segmentation for marketing, forecasts for sales and revenue, Optimization for risk management, etc.
Did you know?
There is no match for the robust curriculum. Digital nest offers for a data science course, as mentioned above. The curriculum is designed by the faculty of data science, so the course structure is one of a kind, offering students the real-time application induced teaching-learning experience. The data science course structure is bifurcated into 6 modules: R-Programming, SQL, Machine Learning, Python, and Power BI.
Why learn and get certified in Python?
1.Use data generation sources
2. Work with tools and techniques used for the analysis of structured and unstructured data
3. Understand the differences between Descriptive and Predictive Analytics.
4.Perform Text Mining to generate Customer Sentiment Analysis
Many modules are in great demand for the requirements in the present changing business. The black box is the most powerful technique used to validate against the external factors that are responsible for software issues. The supervised machine learning algorithms include Linear Regression, Logistic Regression, Naive Bayes, Decision Trees, Support Vector systems, and many more. Deep learning is the lineage of Machine learning algorithms. Deep learning is mainly used in Computer vision, Bioinformatics, Audio recognition, and medical analyzing systems. Deep learning algorithms include Convolutional Neural Networks, Artificial Neural Networks, Multiple Linear Regression, Logistic regression, etc. Unsupervised learning in data mining includes Clustering, Neural networks, Principal component Analysis, Local outlier factor, and so on.
All the concepts discussed have been intuited from a fundamental level to an advanced level with practical implementation at every stage of the course allowing every course participant to master the skills irrespective of the background they come from.
Who should attend this Training?
Learn without a career break with live online lectures conducted mostly on weekends or after office hours by BITS Pilani faculty members and experienced industry professionals The curriculum covers areas that prepare you for most lucrative careers in the space of Data Science, Data Engineering and Advanced Analytics. It helps learners master critical skills such as Mathematical modeling, Machine learning, Artificial Intelligence, Product development and scripting languages. Benefit from Case Studies, Simulations, Virtual Labs & Remote Labs that allow learners to apply concepts to simulated and real-world situations. Tools & Technologies covered include Apache Spark, Apache Storm for Big Data Systems/ Real-time Processing, Tableau for data visualization, Tensorflow for Deep Learning and various packages within Python for data processing, machine learning and data visualization.
How will I perform the practical sessions in Online training?
For online training, US GlobalSoft provides the virtual environment that helps in accessing each other’s system. The detailed pdf files, reference material, course code are provided to the trainee. Online sessions can be conducted through any of the available requirements like Skype, WebEx, GoToMeeting, Webinar, etc.
Python Course Syllabus
Module 1 :Intro to Data Science
Part A :Learning Objectives:
- Get an overview of the world of data science. Get acquainted with various analysis and visualization tools used in data science.
- What is Data Science?
- Analytics Landscape
- Life Cycle of a Data Science Project
- Data Science Tools & Technologies
Hands-on: No hands-on
- Intro to R Programming
- Installing and Loading Libraries
- Data Structures in R
- Control & Loop Statements in R
- Functions in R
- Loop Functions in R
- String Manipulation & Regular Expression in R
- Working with Data in R
- Data Visualization in R
- Case Study
- Know how to install R, R Studio and other libraries
- Write R Code to understand and implement R Data Structures
- Write R Code to implement loop and &control structures in R
- Write R Code to read and write data from/to R.
- Read data not only from CSV files but also using direct connection to various databases
- Write R Code to implement ggplot for data visualization
- Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study
Probability & Statistics Learning Objectives:
This module explores basics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range and get basic summaries about data and its measures, together with simple graphics analysis.
Through daily life examples, you will understand the basics of probability, marginal probability and its importance with respect to data science. Learn Baye’s theorem and conditional probability, and alternate and null hypothesis including Type1 error, Type2 error, power of the test, and p-value.
- Measures of Central Tendency
- Measures of Dispersion
- Descriptive Statistics
- Probability Basics
- Marginal Probability
- Bayes Theorem
- Probability Distributions
- Hypothesis Testing
Advanced Statistics & Predictive Modeling - I
This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.
Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.
- Linear Regression (OLS)
- Case Study: Linear Regression
- Principal Component Analysis
- Factor Analysis
- Case Study: PCA/FA
- With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
- Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling
Prepare for Certification
Our training and certification program gives you a solid understanding of the key topics covered on the Oreilly’s Datascience with R Certification. In addition to boosting your income potential, getting certified in Datascience with R demonstrates your knowledge of the skills necessary to be a successful Python Developer. The certification validates your ability to produce reliable, high-quality results with increased efficiency and consistency.