About Us

DSA Labs is a unit of i-POINT focussed on developing talent in Data Science and Analytics. We have developed a rich portfolio of short-term courses in Data Science and Analytics aimed at creating industry ready professionals in BI Reporting, Analytics and Decision roles.

Why DSA Labs?

Since we started i-POINT in 2008, we have a track record of training over 15,000 plus students in professional skills needed by industry and our alumni have been working for Tier-1 global companies in India and abroad.

We created DSA Labs because we saw an opportunity to create industry ready talent in the field of data science and analytics through immersive learning experience at an affordable cost unlike many who offer passive learning experience through video lectures for comparable costs.


Our proven hands-on minds-on training approach is guaranteed# to deliver best returns on your investment. The following are key characteristics of every course/program at DSA Labs

  • Instructor Led Training (ILT) in small batches for individual attention
  • Course curriculum aligned to industry skill needs for various job roles
  • Practical training cycle of Tell me - Show me - Let me do it
  • State-of-the-art computing infrastructure (SW&HW)
  • Training by industry experts
  • Flexible course plans (fast-track/regular)
  • 1-year access to course materials on LMS

Currently, we have one training facility in Mangaluru city and will be soon expanding to Bengaluru.


#Contact us for money-back guarantee scheme for registrations in 2020. Conditions apply.


Programs


Certification in Data Analysis & Visualization (CDAV)

Course ID: DS-C-001

Program Summary: Industry readiness program in data processing, visualization and analysis for graduates/post-graduates aspiring to make a career in the field of data science and analytics


Program Level: Foundation Duration: 3-6 months Classroom Hours: 20hrs Lab Hours: 20hrs


Delivery Mode: Instructor Led Training + Online Self-Study Reference Material


Pre-requisites: None. Basic MS-Excel is advantageous but not necessary


Industry Job Roles Alignment: Junior Data Analyst, Data Process Executive, BI Analyst, Reports Analyst


Job Skills Covered:

  • Data querying using SQL
  • Data Pre-processing - Importing, Data scraping from web pages/PDF files, Data Cleaning and Data Wrangling, Basic ETL (Exactions, Transformation and Loading) using Power Query
  • Data Exploration and Discovery - Binning, Summarization, Data Pivoting, Exploratory Statistical Analysis
  • Data Visualization - Creating visual reports (static & interactive) and dashboards

Program Coverage:

Module 1: Introduction to realm of data science and analytics - key industry terminologies, data science process, career opportunities in the data science field, skills needed


Module 2: Relational data base model basics, Data retrieval using SQL, SQL Operators and functions, SQL joins, Data Sorting and Grouping in SQL, Sub-queries, Merging Queries, and Exporting Query Results


Module 3: Importing Data in Excel, Data Cleaning in Excel (duplicate rows, formatting corrections, removing errors and inconsistencies), Data Integration - joining and matching data from two sources, transforming and rearranging columns/rows, Data cleaning using OpenRefine - Facets and Filters, Common Transformations, Clustering and Editing


Module 4: Foundation terminologies and concepts in Data Analysis, Grouping and Summarizing, Creating Crosstab reports (Pivot Tables and Pivot Charts), Charts and Graphs, Creating Visual Infographics and Dashboards using Excel. Data transformations, modelling and visualization using Power BI


Tools Covered:

Delivery Team Profile: Trainers with over 20+ years of industry experience in IT Services Industry

Contact Us to talk to a counsellor

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Certification in Data Analytics and Machine Learning in R (CDAML-R)

Course ID: DS-COMBO-001

Program Summary: Industry readiness program in exploratory data analysis, visualization, descriptive analytics, predictive analytics, and machine learning methods using R tool, for graduates/postgraduates aspiring to make a career in the field of data science and analytics


Duration: 4-5 months Contact Hours: Total 60 hrs 4 hrs/week


Delivery Mode: Instructor Led Online Training (WebEx) + Online Self-Study Reference Material


Pre-requisites: None


Industry Job Roles Alignment: Data Analyst, Machine Learning Engineer


Job Skills Covered:

  • Foundation skills in probability and statistics
  • Data Exploration and Discovery - Binning, Summarization, Data Pivoting, Exploratory Statistical Analysis
  • Advanced statistical analysis using Excel Analysis toolset - Hypothesis Testing - means (one sample and two sample tests), multi category tests (Chi-square, ANOVA, F test)
  • Advanced statistical techniques for predictive analysis in R - linear regression models (single and multi), time series models, Testing model effectiveness, Correlation analysis
  • Application of Machine Learning methods and algorithms (Supervised and Unsupervised) in R for classification, numeric predictions, pattern recognition in data and clustering tasks
  • Evaluation of model accuracy and model performance

Program Coverage:

Module 1: Overview of Data Science and Analytics, Methodology, Tools and Applications


Module 2: Foundation skills in probability and statistics - classical probability, experimental probability, conditional probability, Bayes theorem, probability distributions


Module 3: Basics of R for data analysis- Importing data from multiple data sources, basic data manipulation - grouping, aggregation and summarization, custom functions, visualization of data in R, univariate and bivariate analysis


Module 4: Hypothesis testing

Inferential statistics - sampling distributions, central limit theorem, standard error of statistic, hypothesis testing, Type 1 and Type 2 error, power of a test, p-values, Z test, T tests (one and two sample tests) , Chi-Square and ANOVA


Module 5: Predictive Analytics with classical models

Classical models for predictions- Simple Linear Regression model, Multiple Linear Regression, Model building and evaluating linear regression models

Logistic regression models, MLE concept, building and evaluating logistic models in R

Time series models - Moving average, exponential smoothing, Holt-Winters forecasting models, ARIMA model


Module 6: Concepts and application of popular supervised learning methods

Classification algorithms: Lazy Learning: KNN (K Nearest Neighbours), Probabilistic learning: Naive Bayes, Divide and Conquer: Decision Trees (C5.0) and Classification Rule Learners (1R and RIPPER)

Regression algorithms: Regression Trees and Model Trees

Dual purpose algorithms: Artificial Neural Network (ANN), Support Vector Machines (SVM)


Module 7: Unsupervised Learning Methods

Pattern detection using Association rules, k-means clustering and hierarchical clustering


Module 8: Model evaluation, performance measurement and improving model performance

Confusion matrix, performance measures, Ensemble methods - Bagging, Boosting and Random Forests


Tools Covered:

Delivery Team Profile: Trainers with over 20+ years of industry experience in IT Services Industry

Certification in Data Analytics and Machine Learning in Python (CDAML-P)

Course ID: DS-COMBO-002

Program Summary: Industry readiness program in exploratory data analysis, visualization, descriptive and predictive analytics using data analysis tools in Python for graduates/post-graduates aspiring to make a career in the field of data science and analytics


Program Level: Intermediate Duration: 4 months Contact Hours: 60 hrs, 4 hrs/week


Delivery Mode: Instructor Led Training + Online Self-Study Reference Material


Pre-requisites: None


Industry Job Roles Alignment: Data Analyst, Machine Learning Engineer


Job Skills Covered:

  • Foundation skills in probability and statistics
  • Foundation skills in Python programming
  • Python modules for data analysis -Numpy, Pandas, and Matplotlib
  • Data Exploration and Discovery - Binning, Summarization, Data Pivoting, Exploratory Statistical Analysis
  • Advanced statistical analysis- Hypothesis Testing - means (one sample and two sample tests), multi category tests (Chi-square, ANOVA, F test)
  • Advanced statistical techniques for predictive analysis in Python - linear regression models (single and multi), time series models, Testing model effectiveness, Correlation analysis
  • Application of Machine Learning methods and algorithms (Supervised and Unsupervised) in Python for classification, numeric predictions, and clustering tasks
  • Evaluation of model accuracy and model performance

Program Coverage:

Module 1: Overview of Data Science and Analytics, Methodology, Tools and Applications


Module 2: Basics of Python for data analysis- Importing data from multiple data sources, basic data manipulation - grouping, aggregation and summarization, custom functions, advanced tools in Python for data analysis - Pandas, MatPlotLib, NumPy, sklearn, statsmodels


Module 3: Foundation skills in probability and statistics - classical probability, experimental probability, conditional probability, Bayes theorem


Module 4: Hypothesis testing with Python - Inferential statistics - probability distributions, sampling distributions, central limit theorem, standard error of statistic, hypothesis testing, Type 1 and Type 2 error, power of a test, p-values, Z test, T tests (one and two sample tests) , Chi-Square and ANOVA tests for multiple samples. Application using Python libraries


Module 5: Predictive Analytics with Python

Causal models - Simple Linear Regression model, Model building and iterations, OLS Algorithm, obtaining insights from model results, Logistics regression models, MLE algorithm, building and evaluating logistic models in Python

Time series models - Moving average, exponential smoothing, Holt-Winters forecasting models, ARIMA model


Module 6: Concepts and application of popular supervised learning methods

Lazy Learning: KNN (K Nearest Neighbours), Probabilistic learning: Naive Bayes, Divide and Conquer: Decision Trees. Dual purpose algorithms: Artificial Neural Network (ANN), Support Vector Machines (SVM)


Module 7: Unsupervised Learning Methods

k-means clustering and hierarchical clustering


Module 8: Model evaluation, performance measurement and improving model performance

Confusion matrix, performance measures, Ensemble methods - Bagging, Boosting and Random Forests


Tools Covered:

Delivery Team Profile: Trainers with over 20+ years of industry experience in IT Services Industry

Applied Business Statistics (ABS)

Course ID: DS-F-001

Program Summary: Foundation program in applied business statistics for beginners in Data Science field


Program Level: Foundation Duration:1 month Classroom Hours: 20hrs Lab Hours: 0


Delivery Mode: Instructor Led Training + Online Self-Study Reference Material


Pre-requisites: None


Industry Job Roles Alignment: Data Analyst


Job Skills Covered:

Program Coverage:

  • Introduction to statistics and application in business
  • Measurements and Measurement Levels
  • Descriptive Statistical measures - Averages
  • Probability Concepts- Classical and Experimental, Expected Values
  • Probability distributions - Binomial, Poisson, Normal
  • Inferential statistics
    • Sampling and Sampling distributions
    • Estimation and Standard error of the estimate
    • Central limit theorem
    • Hypothesis testing - 1 sample, 2 sample, multiple sample tests (Chi-Square/ANOVA)
  • Regression Analysis - Simple Linear Regression
  • Correlation Analysis

R Programming for Data Science (RPDS)

Course ID: : DS-F-002

Program Summary: Foundation program in R Programming for beginners in Data Science


Program Level: Foundation Duration:1 month Classroom Hours: 8hrs Lab Hours: 8hrs


Delivery Mode: Instructor Led Training + Online Self-Study Reference Material


Pre-requisites: None


Industry Job Roles Alignment:


Job Skills Covered:

Program Coverage:

  • Introduction to R programming language and R Studio Tool
  • R datatypes and objects (scalar, vector, matrices, data frames and lists)
  • Variables and operators
  • Programming constructs - control, decision, and looping
  • Functions/built-in functions
  • File handling -reading and writing data
  • Data manipulation
  • Data visualization Plotting charts and graphs

Python Programming for Data Science (PPDS)

Course ID: : DS-F-002

Program Summary: Foundation program in Python Programming for beginners in Data Science


Program Level: Foundation Duration:1 month Classroom Hours: 8hrs Lab Hours: 8hrs


Delivery Mode: Instructor Led Training + Online Self-Study Reference Material


Pre-requisites: None


Industry Job Roles Alignment:


Job Skills Covered:

Program Coverage:

  • Introduction to Python programming language
  • Variables and operators
  • Programming constructs - control, decision, looping
  • Python data structures - List, Tuple, Dictionaries
  • Functions/built-in functions
  • File handling -reading and writing data
  • Data manipulation
  • Data visualization Plotting charts and graphs
  • Python modules and packages


Frequently Asked Questions

Q1.Do I need to have a degree in Maths, Statistics or Science to learn Analytics?
No. What you need is an affinity to understanding of numbers and we will take care of the rest. Most entry level jobs in business analytics look for good analytical skills and ability to communicate with data. While a degree in Maths and Statistics would be advantageous, but not a must as our certification courses include requisite foundation concepts needed. Some employers may ask for specific degrees, but then there are a whole lot who look for data science and analytics skills along with any degree.
Q2.Do I need to know programming in R or Python to take up courses in Data Science?
Prior knowledge of R or Python will be advantageous of our certificate course in Data Analytics and Machine learning. It is not essential for our beginner level course in analysis and visualization. However, programming is part of the course as well. So, if you have not done programming In R or Python, we will support you to acquire the skill needed for data science work. You can also consider our foundation courses in R and Python programming to develop your depth in programming at a nominal price.
Q3.What kind of career assistance does DSA Labs offer?
We will support every student in tailoring resumes and preparing for interviews. We will also provide leads and connects to employment opportunities in the data science and analytics field through our network. While there are no guaranteed placements, our commitment and efforts are 100% to help deserving students to get jobs in the industry.

Q4.Are the courses online or offline?

There are separate online live classroom training as well as regular face to face training batches.

Q5.Will I get a refund if I discontinue after signing up for the program?

For certain programs we offer money back guarantee scheme in case you have discontinued mid-course (conditions apply). Please speak to our counsellor regarding the scheme. This scheme is our confidence of high quality and commitment to deliver value to our students.

Q6.Can I attend the courses if I am on a regular job?

Our evening batches are scheduled between 4.15-6.30PM. If you can work schedule to attend 2 days in a week in this timeslot, you can attend our courses while on the job. Currently, we do not offer weekend batches. If a group of 5 or more people are interested, we will consider weekend bootcamps as necessary.

Testimonials

What our customer says

Hemalatha

Business Analyst

Akash

Engineering Student

Disha & Cecilia

Commerce Graduates

Contact Us

DSA Labs
A unit of i-POINT Interactive Solutions Private Limited
4-8-739/25 First Floor, Divya Enclave
Off: M.G.Road, Mangalore-575003
Karnataka,INDIA
Phone:+91 824 4259758

For inquiries :
Email :
Call Us : 6366052288