Recommended 10-12 hrs/week
Learn at your own pace with 1-on-1 mentorship from industry experts and support from student advisors and career coaches.
Meet weekly with your personal mentor, with as many additional calls as you need.
Leading Industry professionals provide you with oppurtunity to specialize in your respective field to gain experience and to reshape your skills.
Learn by building a portfolio, including a capstone project and industry design project.
Leverage our dedicated career support team working with 200+ organizations.
Data science Training is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
It’s a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data.It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
For entrepreneurs just starting a company, the problems they are solving are largely around finding the right people and convincing them to join, solving for product/market fit, building a solid and attractive product and looking for cash.
To be a Data Scientist, first of all you need to look at your education background. You need a background in Mathematics and statistics. Further, you should be good at logical reasoning, aptitude and communication skills.
Data Scientist help in a lot of ways–optimizing the budget within certain constraints, uncovering bloat in the budget, finding inefficiencies or problem areas in operations, designing and testing new products & etc.,
We help you save –
Money – As you pay no more 10% of our competitors fee
Time: As this workshop covers 20 days course in just 2 days
Efforts – As you are taken through the real-time experiences and you can skip experimenting on your business.
Module 1: Introduction To DataScience
Module 2: Real Time UseCases Of DataScience
Module 3: Who is a DataScientist??
Module 4: Github Tutorial
Module 5: Skillsets needed for DataScientist
Module 6: 6 Steps to take in 3 Months for a complete transformation to DataScience from any other domain
Module 7: Machine Learning-Giving Computers The ability to learn from data
Module 8: Supervised vs Unsupervised
Module 9: Deep Learning vs Machine Learning
Module 10: Link to get Free Data to Practice?
Module 11: Some Great self Learning DataScience Resources(Books, Tutorials, Videos, Papers)
Python Fundamentals begins with acquiring an in-depth knowledge of the Python programming language. By the end of the week, students will be expected to program intermediate level scripts in Python.
Module 1: Introduction To Python
Module 2: “Hello Python Program” in IDLE
Module 3: Jupyter Notebook Tutorial
Module 4: Spyder Tutorial
Module 5: Introduction to Python
Module 6: Variable,Operators,DataTypes
Module 7: If Else, For and While Loops
Module 8: Functions
Module 9: Lambda Expression
Module 10: Filter, Map, Reduce
Module 11: Taking input from the keyboard
Module 1: Create Arrays
Module 2: Array Item Selection and Indexing
Module 3: Array Mathematics
Module 4: Array Operation
Module 1: Introduction to Pandas
Module 2: Series
Module 3: Series indexing and Selection
Module 4: Series Operation
Module 5: Introduction to Pandas
Module 6: Data Frames
Module 7: Data Collection from CSV, JSON, HTML,excel
Module 8: Data Merging,Concatenation, join
Module 9: Group By and Aggregate Function
Module 10: Order By
Module 11: Missing Value Treatment
Module 12: Outlier Detection and Removal
Module 13: Pandas builtin Data Visualisation
Visualization (matplotlib & seaborn)
we’ll begin curriculum focused on various data visualization techniques and how they can help us engage and learn from our data using Matplotlib, Seaborn, ggplot
Module 1: Line Plots
Module 2: Scatter Plots
Module 3: Pair Plots
Module 4: Histograms
Module 5: Heat Maps
Module 6: Bar Plots
Module 7: Count Plots
Module 8: Factor Plots
Module 9: Box Plots
Module 10: Violin Plots
Module 11: Swarm Plots
Module 12: Strip Plots
Module 13: Pandas Builtin Visualisation Library
This session is dedicated to creating a deep understanding of mathematical concepts we’ll later see in topics like machine learning and statistical analysis. Contrary to the traditional mathematics course, students will learn statistics and linear algebra in a programmatic way to fit a problem’s needs.
Module 1: Descriptive vs Inferential Statistics
Module 2: Mean, Median, Mode, Variance,Std. dev
Module 3: Central Limit Theorm
Module 4: Co-Variance
Module 5: Pearson’s Product Moment Correlation
Module 6: R – Square
Module 7: Adjusted R-Square
Module 8: Spearman’s. Rank order Coefficient
Module 9:Sample vs Population
Module 10: Standardizing Data(Z-score)
Module 11: Hypothesis Testing
Module 12: Normal Distribution
Module 13: Bias-Variance Tradeoff
Module 14: Skewness
Module 15: P Value
Module 16: Z-test vs T-test
Module 17: The F distribution
Module 18: The chi-Square test of Independence
Module 19: Type-1 and Type-2 errors
Module 20: Annova
Module 1: Introduction to Machine Learning
Module 2: Machine Learning Usecases
Module 3: Supervised vs Unsupervised vs Semi-Supervised
Module 4: Machine Learning process Workflow
Module 5: Training a model
Module 6: Validating results
Module 7: Overfitting vs Underfitting
Module 8: Ordinal vs Nominal data
Module 9: Structured vs unstructured vs semistructured data
Module 10: Intro to scikitLearn
Module 1: Regression Vs Classification
Module 2: Linear regression
Module 3: Multivariate regression
Module 4: Polynomial regression
Module 5: Multi-Colinearity,
Module 6: Auto correlation
Module 7: Heteroscedascity
Module 1: KNN
Module 2: Svm
Module 3: Decision Tree
Module 4: Random Forest
Module 5: Performance tuning of Random Forest
Module 6: Naive Bayse
Module 7: Overfitting Vs Underfitting
Module 1: Kmeans
Module 2: How to choose the number of K in KMeans
Module 3: Hands-on
Module 4: PCA
Module 1: Basic of Neural Network
Module 2: Type of NN
Module 3: Cost Function
Module 4: Tensorflow Basics
Module 5: Hands on Simple NN with Tensorflow
Module 6:Image classification using CNN
Job Placements Assistance
Case Study and Live Projects
Hours of course content
Data Science tools and techniques
R/Python. You need to be really good in at least one. Also, learn to reuse your code. SQL. 99% of firms still use relational databases, and interviews still include coding tests on SQL, so learn it! Excel. Technically, this is NOT a programming language, but all senior executives use Excel, so it behooves folks to learn Excel and VBA etc.,
Data science is a new field and you must remember that it is a multi-disciplinary field. It involves knowledge of math, statistics, ML, AI and skills of programming and effective communication. Meet the best-in-class industry professions who’s daily routine is Data Science.
Bachelor’s degree in science, Engineering, Business Administration, Commerce Mathematics or Masters in Mathematics, Statistics
Official Certification of Participation from EduHac Power Learning
Endorsements on your LinkedIn account from EduHac
Life time access to self-paced videos and class recordings
Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.
Contact us using the form on the right of any page on the Eduhac website, select the Live Chat link or contact Help & Support.
All of our highly qualified trainers are industry experts with at least 15 years of experience in training and working in their respective domains. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.
The prep course starts each Monday. Simply click “Enroll now” and select the cohort that best works for you.
The total estimated workload is 40-60 hours, expected to be completed in 4-6 weeks. You may complete the course faster if you have previous programming experience or if you are able to dedicate additional time to the material. You may also need more time to finish if you are brand new to programming or have a constrained schedule—but that’s no problem; you have access to the course as long as you need!
No prior coding experience is required, but to be successful, we recommend that students are already proficient in high-school level mathematics with an openness to learning more advanced concepts where necessary.
You may apply to the Data Science Career Track at any time, even after enrolling in the prep course. As soon as you feel comfortable in Python and statistics, we encourage you to fill out an application and take the admissions technical skills survey. However, students who complete the entire prep course and submit the final project will be fast-tracked through the Career Track application process.
You will be invited to join our broader weekly data science office hours with all current data science students. You will also have access to the community, where you can ask technical questions or seek assistance from fellow classmates or the course community manager, an expert data scientist. Furthermore, you will have the support of a dedicated student advisor, there to assist you throughout the course. Finally, your classmates! You aren’t going through this alone; you’ll have the support of others starting out on their own unique data science journeys.
Yes, once you enroll you will have lifetime access to the curriculum and exercises. You will not, however, have lifetime access to the mentorship. You will receive six mentor calls, one per week, for the expected 4-6 week duration of the course.
You will have 6 weekly mentor calls once the course starts and you will be paired with your mentor before the official start date. If you need additional time to complete, you are able to continue through the curriculum without a mentor beyond 6 weeks for as long as needed as you will have lifetime access.
Due to the short duration of the course and fast paced nature of the curriculum we do not offer refunds for our prep course. If you have a unique situation we encourage you to reach out to your student adviser to discuss potential options.