Data Science

Data Science Course Objectives

Python full coding from scratch

 Visualization with Python

 Statistics - theory and application in business

 Machine Learning with Python - 6 different algorithms

 Multiple Linear regression

 Logistic regression

 Variable Reduction Technique - Information Value

 Forecasting - ARIMA

 Cluster Analysis

 Decision Tree

 Random Forest

 Case studies on Machine Learning (18 case studies)

 SQL queries(with Python)

 Business Presentation of Technical Solution in-front of end client.

 Robotic Automation(with Python)

 CV Building activities

 Interview preparation

 Mock Interview sessions

Data Science Course Syllabus

 

Machine Learning with Python

1:Introduction to Python Programming Language

 Introduction and Installation of Python software Python packages: Pandas, & Numpy

 Concepts of Data frame Filtering

 Loc and iloc for filtering Usage of Boolean in Filtering Appending

2: Data handling in Python

 Handling of Missing values If else statement

 Extra trick of using if else statement Removal of Duplicates

 Frequency Distribution

 Merging – Inner, Outer, Left and Right Binding and Appending

 Descriptive Statistics

 Inbuilt Numeric functions of R

3: More data handling using Python

Pivot Table of Excel in Python Grouping function

 Learning of SQL queries using Python Grouping numeric data

4: Additional functions of Python

 Text functions

 Data cleaning with efficient text functions Inbuilt String functions of Python Reshape functions of Python

5: Statistic

 Everything you want to know about statistics….Well sort of!! Mean, Median, Mode

 Standard Deviation, Variance, Normal Distribution Hypothesis testing

 T-test, Anova, Normality test

6: Linear Regression

 Predictive Analytics – Linear Regression Concepts of Linear Regression

 Simple and Multiple Linear Regression Automatic Dummy Variables creation technique Model Validation parameters

 Model Assumption testing

 Splitting of data for Validation and testing

 Business Case Study with real data to model in Python

7: Linear Regression Practice Case Study

 Participants will be asked to develop a Linear Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.

8: Logistic Regression

Predictive Analytics – Logistic Regression Concepts of Logistic Regression

 Difference between Linear Regression and Logistic Regression Automatic Dummy Variables creation technique

 Model Validation parameters Model Assumption testing

 Splitting of data for Validation and testing

 Business Case Study with real data to model in Python

9: Logistic Regression Practice Case Study

 Participants will be asked to develop a Logistic Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.

10:Time Series Forecasting

 Time series forecasting: ARIMA

 Difference between forecasting and prediction Concepts of time series data

 Concepts of ARIMA

 Descriptive analytics for ARIMA Development of model

 Best model selection Forecasting with the best model Residual analysis

 Business Case Study with real data to model in R software

 Participants will be asked to develop a model in presence of the instructor.

11: Cluster Analysis

 Unsupervised Machine Learning with R Cluster Analysis: Concepts

 Cluster analysis with R – K Means, Hierarchical etc.

12: Decision Tree and Random Forest

 Concepts of Decision Tree Decision Tree with Python Concepts of Random Forest Random Forest with Python

R Programming

1: Introduction to R Programming Language

 Introduction and Installation of R software R packages

 Concepts of Vector – Numeric, Character, and Factor Concepts of Data frame

 Filtering

 Usage of Boolean in Filtering Sorting

Reshape of data using Tidyr package

3: More data handling using R

 Pivot Table of Excel in R Table function

 Count function of plyr package Learning of SQL queries using R Grouping numeric data

 User defined functions (Macros) in R Visualizing of Data

2: Data handling in R

 Handling of Missing values If else statement

 Extra trick of using if else statement Removal of Duplicates

 Merging – Inner, Outer, Left and Right Binding and Appending

 Text functions

 Data cleaning with efficient text functions Inbuilt Numeric functions of R

 Inbuilt String functions of R Inbuilt other functions of

4: Additional functions of R

 Date functions with Lubridate package Apply functions

 User defined functions (Macros) in R Visualizing of Data

Data Analytics with MS-excel

1: Introduction to Excel

Introduction to Excel Workbook and worksheets Entering data into the spread sheet Undo & Redo Adding Comments

Formatting and conditional formatting All types of borders Moving & Coping and inserting data Finding and replacing Filtering and Sorting of data

Logical operators Practice sessions

3: Analytics with Excel

Pivot Table

Data manipulation with pivot tables Pivot table charts Data visualization with Excel

Practice sessions

2: Different Functionalities of MS Excel

Text to column V-look up Duplicate removal Concatenate

Functions of excel – Logical, Mathematical, Statistical, Others Practice sessions

4: Advanced Analytics with Excel

Pivot Table

 Functions of excel – Financial functions What if analysis – Goal Seek, Solver etc. Macros

 Analytics using Excel Practice sessions

 

Modify existing databases and database management systems (DBMS) or instruct programmers and other analysts to make essential changes.

Write and code logical and physical database descriptions and specify identifiers of database to management system or direct other colleagues in coding descriptions.

Review project requests describing database user needs to estimate time and cost required to accomplish project.

Review data results to ensure accuracy. Configure data visualizations for stakeholders

Provide data analysis and standard reporting support, which includes the ability to extract data from various sources and data stores by executing light business coding (SQL, VBA, Unix, etc.) and system parameter setting, perform ad-hoc queries and develop/automate financial/statistical models using a variety of known software applications and tools (Excel, Access, etc.)

Support the use of data science and machine learning within the various PSE engineering DevOps teams.

Manipulate and analyze complex, high-volume, high-dimensionality data from varying sources using a variety of tools and data analysis techniques

Translates business requirements throughout the development process, delivers solutions in accordance with business strategies, standards, and processes.

Develop business cases for R&D initiatives, provides expert advice to product managers, developers, architects and business partners on data science use cases and options.

Architect highly scalable distributed systems, using different open source tools.

Working with lambda architectures and batch and real-time data streams.

Understand high performance algorithms and Python statistical software and brief team.

 

 

Artificial Intelligence and Machine Learning's Top 7 Uses and Applications in Industry

Artificial intelligence has altered the market environment drastically. What began as a rule-based automation system is now capable of simulating human interaction. Artificial intelligence is characterized not only by its human-like skills. As compared to human equivalents, an advanced AI algorithm provides much better speed and reliability at a much lower cost.

7 Business Implementations of Artificial Intelligence:

 Artificial intelligence is no longer just a hypothesis. It does have a wide range of applications. According to a 2016 Gartner survey, at least 30% of businesses worldwide will use AI in at least one portion of their sales processes by 2020. Artificial intelligence is now being used by companies all over the world to enhance their processes and boost sales and benefits. We talked with a few experts in the field to get their views on AI applications. The following are some of our findings:

1)Business Users: 

Artificial intelligence (AI) is still a hot topic in the tech world, and it's making inroads into other fields like healthcare, industry, and gaming. People would become more familiar with how AI will help businesses rather than taking away their jobs as a result of AI-powered chatbots in businesses. AI can be integrated into interfaces to improve how they receive and understand data from an analytical perspective.

Chatbots, in particular, are always on, delivering smart and versatile analytics via standard messaging tools and voice-activated interfaces via conversations on mobile devices. This greatly reduces the time it takes for all business users to collect data, speeding up the pace of business and streamlining the way analysts spend their time, preparing businesses for the increasing data demands of the future.

2)Artificial Intelligence in eCommerce:

Artificial Intelligence technology offers e-commerce companies a competitive advantage and is becoming more commonly accessible to businesses of all sizes and budgets. AI software uses machine learning to automatically tag, organize, and visually scan content by marking image or video attributes.

Using artificial intelligence, shoppers may find similar items based on size, color, shape, or even brand. Every year, AI's visual capabilities increase. The app will effectively assist the customer in identifying the product they want by first obtaining visual cues from the uploaded imagery. Many e-commerce retailers are already developing their AI capabilities, and I only expect this to grow in the future.

3)AI to Improve Workplace Communication:

The current state of business communication is overburdened with content, platforms, resources, and so-called solutions, making it impossible for individuals (and companies) to achieve their goals while also undermining work-life balance. Artificial Intelligence can assist companies in enhancing internal and external communication by facilitating individual personalization for each employee, allowing for improved concentration and productivity.

With AI personalization, each person will be empowered by an intelligent virtual assistant who will support them with mundane or repeatable tasks, save time by knowing their needs and priorities, and suggest the next-best-action to take...all without requiring any additional effort. In the short to long run, business processes will improve, innovation will grow as employees will clear their tasks, and stress may decrease.

4)Human Resource Management:

AI and machine learning are going to change the way HR and recruiting is done in every company, and it's going to be great. For two basic reasons, HR is likely to be one of the first areas of the industry to benefit from AI. First, HR has a wealth of high-quality data, and second, HR is one of the few sections of any business that is both required and undervalued.

If parts of the hiring and HR roles can be automated, HR employees would be able to spend more time personally communicating with people in the organization or potential hires, which is important for a great HR department. It may sound counterintuitive, but the more Artificial Intelligence an organization uses in HR, the more ‘human' it becomes.

Artificial Intelligence can effectively remove all of the "worst" aspects of an HR professional's job (mundane screening, time-consuming paperwork, and vexing data entry) while also offering valuable tools and insights to enhance their work. HR is one of the first places to encounter the 4th Industrial revolution because of its automatic generation of high-quality data and the amazing benefits of AI.

5)AI in Healthcare:

Artificial intelligence will have a huge impact on the healthcare industry and how healthcare-related companies use AI in the coming yearOur application will be able to take the data we've obtained from patients and use AI to clinically innovate to enhance patient outcomes even more. AI increases quality and patient safety by increasing predictability, accuracy, and reliability. We use AI as a decision-augmentation tool in the software, but it should not be granted full autonomy without human involvement and guidance. While it won't be able to replace doctors and nurses, it will help them be more effective, productive, and comfortable on the job by removing the cognitive load from them, which improves morale and reduces stress and anxiety.

6)Intelligent Cybersecurity:

Artificial Intelligence is making tremendous progress in the area of cybersecurity. Although AI is still in its infancy in cybersecurity and cannot always efficiently resolve all problems, it has proven to be effective in data protection. AI helps businesses to identify security bugs or suspicious user activity in business applications such as ERP and financial systems.

A system of behavior anomalies detection in computer systems is similar to the world's most safe airport: on the way there, the security system has enough time to investigate your identity; you are checked by cameras, and you are intercepted if you display any signs of risk. Deep learning can detect whether a user has engaged in any suspicious actions. As a result, even if perpetrators obtain access to a victim's system, they begin performing acts that are not typical of them, ensuring that they do not go unnoticed and that their harm is minimal.

7)Artificial Intelligence in Logistics and Supply Chain:

Physical artificial intelligence, when combined with consumer data and analytics, eliminates friction in the customer experience. Artificial intelligence allows companies to act on customer data to enhance supply chain processes in several ways. Consumers are hungry for AI thanks to mobile technology and the "Uberization" of things.

Consumers expect retailers to produce products quicker, and suppliers and fulfillment centers would expect the same. Supply chains will meet orders seven days a week thanks to autonomous vehicles and robotic picking systems. Consumers demand delivery on nights and weekends over the next five years, making the word "business days" redundant.

FAQs

 

 What is Data Science ?

 Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. . A Data Scientist will look at the data from many angles, sometimes angles not known earlier

 What will I learn in this Data Science training?

 Roles & responsibilities of a Data Scientist.

 Testing, assessing and managing data of a organization.

 Prediction/Forecast and analysis breakdown using various tools .

 Sampling techniques.

 Working with recommender software and systems

 Installation and working with analytics tools

 Linear and logistic regression approaches.

 Deploying clustering for analysis.

 What are the Job roles for candidates with knowledge in Data Science

 Data Engineer

 Data Scientist

 Data Visualizer

 Data Analyst

 Business Analyst

 Industries for Data Analytics Jobs in India

 E-Commerce

 Banking

 Finance

 Healthcare

 Telecommunications

 Travel & Tourism

 Who is the father of data science?

 William. S

 What is the syllabus of data analytics?

 The Data Science syllabus essentially comprises of Mathematics, Statistics, Coding, Business Intelligence, Machine Learning algorithms, and Data Analysis.

 Which Analytics course is best?

 Data Analyst with R.

 Which Certification is best for Data Analyst?

 Associate Certified Analytics Professional, Certification of Professional Achievement in Data Science, Certified Analytics Professional, etc.

  How can I become a data scientist in 6 months?

 You have to analyze yourself and try to find out what are the necessary programming skills you need to have.

 Do data scientists use Python?

 Yes, data scientists use Python for Data Science.

 How do I start learning Data Science with Python?

 First, learn Python fundamentals. Practice Mini Python projects. Learn Python Data Science Libraries. Build a Data Science Portfolio. Apply advanced data science techniques.

 

Our Objectives:

Provide high quality skill based training courses in the field of IT & non-IT

Aim to offer something really that adds values to your career from any professional background and any stage in their careers.

Provide more and better quality training courses for students.