Job oriented courses

Data Science Course Objectives

  • Apply quantitative modeling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.
  • Apply principles of Data Science to the analysis of business problems.
  • Use data mining software to solve real-world problems.
  • Employ cutting edge tools and technologies to analyze Big Data.
  • Demonstrate use of team work, leadership skills, decision making and organization theory.

Data Science Course Syllabus

Data Science with Python Programming

  • 1: Import data into SAS and manipulate them

  • Introduction to SAS
  • 5 main windows of SAS Importing data into SAS Data step Vs Proc Step
  • Conditional processing: If, else if, and else statement Boolean in if else statement
  • Where statement
  • All types of merging using data step

  • 2: Different Proc statements

  • Proc Print
  • Proc means and all the options Proc univariate
  • Proc Freq Proc sort
  • Removal of duplicates

  • 3: SAS Functions – Date, Numeric and Character

  • Difference between functions and Proc Inbuilt Numeric functions of SAS Inbuilt Character functions of SAS Inbuilt Date functions of SAS

  • 4: SQL in SAS and other advanced functionalities

  • LinSQL queries Merging with SQL Macros in SAS
  • Output Delivery System in SAS

  • 5: Full Statistic refresher course

  • 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 with SAS

  • 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 SAS software

  • 7: Additional Case study on Linear Regression

  • 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 with SAS

  • 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 R software

  • 9: Additional Case study on Logistic Regression

  • 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 with SAS

  • 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 R
  • C5.0 algorithms and R part Concepts of Random Forest Random Forest with R

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
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Students Feedback for Data Science in Ahmedabad

Resham Rajpal

The training at Apponix made me more confident in Data analysis. I have completed Data Science training form here. Excellent classes that helped a lot.

Data Science Expert
Suryadeep C

My experience with Apponix was really great. The trainer provided a lot of practical training. He is an expert. Each topic was taught through proper practical classes.

Data Science Analyst
Mahesh Mahi

I took Data Science training from the institute. I’m happy to make Apponix as my choice. Too good labs and sessions are handled here.

Data Science Analyst
subrat Mr Shubhojit- Senior Data Scientist

Data Science Trainer 12+ Years Of Working Experience in MNC.

Data Science Trainer Profile

  • 12+ years of Experience in Data Science, currently spearheading the efforts.
  • Trained more than 2000+ students on Data Science at Apponix.
  • 5-star rating from all Data Science students.
  • Well versed in Data Science.
  • Excellent training delivery skills with an ability to present information well.
  • Demonstrable experience of being student focused and completing projects to hit deadlines and targets.
  • Demonstrable proof of enthusiasm, initiative, creativity and problem solving.
  • Demonstrable experience in delivering quality training on Data Science.
  • Excellent practical experience.

Apponix Ratings

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Student Review

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Lakshana Senthil
Data Engineer

Management is very good, flexible with timings. Teaching pattern is easy and understandable.

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Ajith Bahadhur
Data Analyst

I took training for Data Science from Apponix institute, tutor has good knowledge on the subject & very experienced, he makes understand every concept and he will clear the doubts again & again, i gained more knowledge on Data Science.

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Bijal Patel
Data Analyst

Best institute for Data Science. Trainer has very good knowledge in Data Science.

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Vamshi Krishna
Data Engineer

Sir is very helpful and takes every step to make the students understand topics.

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Taniya Nabi
Data Analyst

It is good to learn Data Science here and the tranier is excellent.

Salary expectation after completing Data Science course

As there is a growing demand for Data Science Engineers, the salary is also constantly increasing for Data Science skills,
As per Average salary for Data Scientist is Rs 9,12,453 Per year.

Career after Data Science course

Bangalore is a Silicon Valley of India, it has large number of IT companies spread across Bangalore, you should not have any doubt on shortage of Data Science jobs in Bangalore. IT technologies is growing and there a huge demand for Data Science engineers. Data Science has more than 70% of the markets share in terms of providing services.

aws responsiblity

Data Science Job Responsibilities

  • 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.


  • 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 Sciencearrow
  • Data Engineer
  • Data Scientist
  • Data Visualizer
  • Data Analyst
  • Business Analyst
  • Why you should choose Apponix?
  • Apponix has best experienced and multi skilled trainers with years of experience in the industry.
  • Apponix has tied up with many companies and consultancies and recruiting agencies to provide placement assistance for all our students.
  • 100% job guarantee for this course.
  • Trained more than 2000 candidates in this course and placed them.
  • Excellent lab facilities for every candidate and all our classrooms are Air-Conditioned to make students comfortable while learning.
  • Each student is given a computer to practice throughout the duration of this course.
  • Industries for Data Analytics Jobs in India
  • E-Commerce
  • Banking
  • Finance
  • Healthcare
  • Telecommunications
  • Travel & Tourism
  • Top 10 Companies that hire Data analytics professionals
  • IBM, Dell, Microsoft, Infosys, Wipro, JP Morgan, Amazon, Flipkart, Snapdeal & TCS
  • Who is the father of data science?arrow
  • William. S
  • What is the syllabus of data analytics?arrow
  • The Data Science syllabus essentially comprises of Mathematics, Statistics, Coding, Business Intelligence, Machine Learning algorithms, and Data Analysis.
  • How long is the Data Science course?arrow
  • The Data Science course is a training program of around six to twelve months.
  • Which Analytics course is best?arrow
  • Data Analyst with R.
  • How much do Coursera courses cost?arrow
  • For an individual course of around 4-6 weeks, it costs about $29-$99. For a specialized program, it costs $39-$79.
  • Which Certification is best for Data Analyst?arrow
  • Associate Certified Analytics Professional, Certification of Professional Achievement in Data Science, Certified Analytics Professional, etc.
  • What are the fees for the Data Science course?arrow
  • The fee structure ranging from Rs.30000 to Rs.100000.
  • How can I become a data scientist in 6 months?arrow
  • 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?arrow
  • Yes, data scientists use Python for Data Science.
  • How do I start learning Data Science with Python?arrow
  • First, learn Python fundamentals. Practice Mini Python projects. Learn Python Data Science Libraries. Build a Data Science Portfolio. Apply advanced data science techniques.
  • Which institute is good for Data Science in Bangalore?arrow
  • Apponix Technologies is the best Data Science training institutes in Bangalore which provides services from training to placement.
  • How Data Science and Cloud Computing are connected

    Cloud Computing and Data Science essentially go Hand in Hand. Data scientists analyze the different types of data that are stored in the let's watch how cloud computing and Data analytics technology compliments each other for better value performance

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