Next Badge Starts : 29 May 2022
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India's Largest Data Science Learning Platform
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What We Offer
All Pre- Requisite Covered
10 Assured Certificates
320+ Hours of Training
Instructed by Google, IMDB Alumni
Weekly Live Classes
1:1 Mentor Support
Doubt Resolution over Zoom
Free 2,000 Resources/ Tools worth ₹50,000
10+ Capstone Projects
5+ Live Projects
650+ assignments and quizzes
Free Lifetime Updates
The Fast Track to Lucractive Data Science Job
200+ Hiring Partners
100% Job Opportunities
₹6 LPA Min Package
Skillcept Alumni Works at
Data Science Learning Path
Hours of Lecture
Module 1 : Python Programming Masterclass for Data Science
This Module Teaches You The Python Language in Depth. Includes Python Online Training With Python 3.
- Fundamental understanding of the Python programming language.
- The skills and understanding of Python to confidently apply for Python programming jobs.
- Acquire the pre-requisite Python skills to move into specific branches - Machine Learning, Data Science, etc..
- Add the Python Object-Oriented Programming (OOP) skills to your résumé.
- Understand how to create your own Python programs.
- Learn Python from experienced professional software developers.
- Understand both Python 2 and Python 3.
Module 2 : Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning.
- The module provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
Module 3 : Machine Learning
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.
- Build artificial neural networks with Tensorflow and Keras
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Implement machine learning at massive scale with Apache Spark's MLLib
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
Module 4 : Deep Learning with Tensflow 2.0
Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case.
- Gain a Strong Understanding of TensorFlow - Google’s Cutting-Edge Deep Learning Framework
- Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
- Set Yourself Apart with Hands-on Deep and Machine Learning Experience
- Grasp the Mathematics Behind Deep Learning Algorithms
- Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
- Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
- Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding
Module 5 : Artificial Intelligence Masterclass
Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models.
- How to Build an AI
- How to Build a Hybrid Intelligent System
- Fully-Connected Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Variational AutoEncoders
- Mixture Density Network
- Deep Reinforcement Learning
- Policy Gradient
- Genetic Algorithms
- Evolution Strategies
- Covariance-Matrix Adaptation Evolution Strategies (CMA-ES)
- Meta Learning
- Deep NeuroEvolution
Module 6 : Data Analysis & Visualization: Python | Excel | BI | Tableau
Connect to data, clean & transform data, analyse and visualize data.
- Connect to Kaggle Datasets
- Explore Pandas DataFrame
- Analyse and manipulate Pandas DataFrame
- Data cleaning with Python
- Data Visualization with Python
- Connect to web data with Power BI
- Clean and transform web data with Power BI
- Create data visualization with Power BI
- Publish reports to Power BI Service
- Transform less structured data with Power BI
- Connect to data source with excel
- Prep query with excel Power query
- Data cleaning with excel
- Create data model and build relationships
- Create lookups with DAX
- Analyse data with Pivot Tables
- Analyse data with Pivot Charts
- Connect to data sources with Tableau
- Join related data and create relationships with Tableau
- Data Cleaning with Tableau
- Data analysis with Tableau
- Data visualization with Tableau
Module 7 : Business Intelligence Analyst
The skills you need to become a BI Analyst - Statistics, Database theory, SQL, Tableau – Everything is included.
- Become an expert in Statistics, SQL, Tableau, and problem solving
- Boost your resume with in-demand skills
- Gather, organize, analyze and visualize data
- Use data for improved business decision-making
- Present information in the form of metrics, KPIs, reports, and dashboards
- Perform quantitative and qualitative business analysis
- Analyze current and historical data
- Discover how to find trends, market conditions, and research competitor positioning
- Understand the fundamentals of database theory
- Use SQL to create, design, and manipulate SQL databases
- Extract data from a database writing your own queries
- Create powerful professional visualizations in Tableau
- Combine SQL and Tableau to visualize data from the source
- Solve real-world business analysis tasks in SQL and Tableau
Module 8 : Financial Engineering and Artificial Intelligence in Python
Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!
- Forecasting stock prices and stock returns
- Time series analysis
- Holt-Winters exponential smoothing model
- Efficient Market Hypothesis
- Random Walk Hypothesis
- Exploratory data analysis
- Alpha and Beta
- Distributions and correlations of stock returns
- Modern portfolio theory
- Mean-Variance Optimization
- Efficient frontier, Sharpe ratio, Tangency portfolio
- CAPM (Capital Asset Pricing Model)
- Q-Learning for Algorithmic Trading
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Frequently Asked Questions
No, there are no prerequisites for enrolling. Everything you need to know is included in the program.
We start the whole learning process from scratch. This is one of the things that make us different from other platforms that consider mathematics and statistics a prerequisite.
We explain it all, step-by-step, including how to download and install the necessary software (e.g. Excel, Anaconda, Jupyter Notebook, Python, R, MySQL, Tableau Public, etc.)
Your course is valid for one year. You can finish this course anytime in this one year.
This is one of the ‘harder’ questions.
What usually happens is that most students prefer to pick the parts that are interesting/relevant for them. Therefore, we can’t really give you an exact time frame as the concept of ‘completing the program’ seems different for every student.
However, there are several main factors which drive the duration:
- whether the student is working;
- his/her motivation;
- prior experience.
The biggest determinant seems to be 1) whether the student is working.
Employed people usually devote several hours per week to the program, thus the duration of their studies is longer.
Regarding 2) motivation, we are trying to incentivize students to be more engaged, but at the end of the day, that depends on the individual.
Finally, 3) prior experience – this is important. Some students have done a bit of programming, others – statistics, so they can skip some parts. Overall, they spend less time on the program as a whole and can complete it within a month or two. Assuming that a student starts from scratch, works part-time, and completes all courses and exercises, 6-7 months are sufficient.
However, keep in mind that we expand our program on almost monthly bases, so the time frame to ‘complete the program’ is continuously increasing.
Yes, you can.
We recommend using a computer for taking the course as the programming parts of the course are not well suited for learning on a mobile device.
This means that any prospective employer can contact us or visit verify certification URL to receive official confirmation that you have completed the data science program and receive a list of the skills you have acquired.
For the time being, our platform shows progress for the individual courses!.Once you have completed all individual courses, you will have completed the whole program.