8+ real-world projects
Projects and workshop relevant to business problems
1:1 Mentorship by Analytics Vidhya & KPMG
12+ live sessions by Industry Experts
8 intensive hands-on workshops
4+ hackathons to apply your learnings
12+ industry webinars across domains (Telecom, BFSI, Healthcare, etc)
Live coding & hack sessions by Industry Experts
Reinforce your learnings on weekly-basis
Resume & Interview Preparation
Personalized mock interviews & feedback
Get exciting hiring opportunities with Top Companies including KPMG and KPMG Global services*
We are thrilled to collaborate with Analytics Vidhya for AscendPro program, through which we will jointly offer learning modules that will aim at solving business problems with data & technology. In today’s world, data science technologies can give organisations the ability to fully capitalise on the use of the data they generate every day around them and this course is designed keeping in mind how data science can be leveraged for quantifiable business outcomes. With the combined skillsets of both business and technology, the AscendPro program provides a holistic approach to learning data science thus enabling the candidate for a confident entry into the world of analytics.
Learn how various business use cases and implementation aspects of data science and machine learning across the Consumer markets and Telecom value chain.
Learn how advanced analytics is leveraged by this sector to develop models for decision making and take proactive actions for better business outcomes
Learn about how Fintech and AI solutions are transforming the conventional financial industry.
Learn how Pharma companies are measuring the effectiveness of sales and marketing efforts with data and analytics
Each Hackconf is of 3 full day and there are a total of 4 HackConf where you
3-days live interactive session by experts to build industry perspective
Work on real-world datasets with exciting hackathon problems
4- Intensive workshops to build your real world hands-on skills.
Evaluate your week long learnings through assessments.
12+ Modules starting from basics to the most advanced machine learning topics
Overview Data Science and Application
Common terminologies of Data Science
Various roles within Data Science
Essential Stages of Data Science Life Cycle
Organizational challenges while building Data Science projects
Python Basics Programming (Conditional, Looping, Functions)
Pandas, Matplotlib, Seaborn, regular expression, beautifulsoup
Basics of databases
ACID and BASE properties of a database
Working with SQL, Extract data from databases containing multiple tables
Performing Data Analysis using SQL
SQL vs NoSQL databases
Different types of NoSQL databases
Querying, Aggregation & Indexing in MongoDB
Replicate & Share data in MongoDB
Best practice to perform hypothesis generation
Hypothesis Building and Framework
How to Build Comprehensive Hypothesis set
Univariate, Bi-variate and Multivariate analysis
Work with different type of tests like t-test, z-test, chi-square test, anova
Work with Missing values, outliers, data pre-processing
Learn Important ML Basics Concepts (Train, Test, Validate, Bias , Variance, Overfitting, Underfitting)
Work with Evaluation metrics (Classification and Regression both)
Work with different validation techniques
Perform data cleaning and Preprocessing
Linear Models, Decision Tree, k-NN
Math Behind each Machine Learning Algorithm
Building Classification and Regression Models
Hyperparameter Tuning to improve model
Loading datasets and establishing table relationships
Work with different type of charts and dashboards
Working with Map visualizations and other advanced charts with drill down functionalities
Working with power query for data manipulation
Writing DAX expressions
Understand feature engineering for structured and unstructured data
Perform feature extraction, generation and transformation techniques
Explore the basic and advanced ensemble techniques (rank averaging, random forest and more)
Introduction to unsupervised learning and clustering
Working of clustering algorithms (k-means clustering)
Evaluation metrics for unsupervised learning problems
Public Vs Private Cloud
IaaS vs PaaS Vs SaaS
AWS Global Infrastructure
AWS Compute Services - EC2, AWS Lambda
AWS Storage Services - S3, DynamoDB, RedShift
AWS Security Policies
Monitoring & Analysis - AWS CloudWatch
Building Scalable Models
Introduction to Distributed Computing
RDDs & DataFrames
Understanding Spark Execution
Building Classification & Regression Models
Building ML Pipelines
Candidates can pay the program fee through Netbanking, Credit/Debit cards, Cheque or DD. Also, with our corporate financial partnerships avail education loans at 0% interest rate*.
KPMG in India, a professional services firm, is the Indian member firm affiliated with KPMG International and was established in September 1993. Our professionals leverage the global network of firms, providing detailed knowledge of local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.
KPMG in India offers services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment.
KPMG Lighthouse is our global center of excellence for data-driven technologies, where we help clients transform their business by unlocking business value and addressing their Growth, Risk and Cost strategies.
Our portfolio of services focuses on the latest data science and computing techniques to help solve our clients’ specific business needs. A combination of analytics experience, trustworthy data, and industry/functional knowledge frame the needs and develop continuously self-improving offerings.