Data Analytics
Data Analytics
Advance your career with Sabik’s Data Analytics training! This course gives you Python, R, machine learning & data visualization skills. The Data Science training course also covers big data analytics. It works for beginners and those with experience already. Flexible live classes are offered. (You can with or without video.) Learn at your own pace, whenever you want. You’ll master essential tools. Plus, you’ll get expert mentors and hands-on projects. Graduate fully ready to tackle real-world data challenges and lead innovation in your field!
Available Programs
Internship
Job Guarantee Program
Training
Rs. 25,000
*90 days of access
JGP
Rs. 1,00,000
*90 days of access
Upcoming Batches
Date:
28th November
Time
08:00 AM TO 09:00 AM
Program Duration:
3 Months
Learning Format:
Training
Course Curriculum
Master in Data Science Overview
Unlock Your Data Potential with Sabik’s Data Science Training Course!
We have a curriculum that covers everything—yes, everything! From basic data analysis and programming to those fancy machine learning (oh, and big data) techniques everyone talks about, get ready to dive into hands-on projects. They’re just like real-world problems.
But there’s more! You get personalized guidance from top industry experts. We mix comprehensive learning with practical experience. This means you will have all the skills you need to shine in the fast-paced world of Data Science.
Transform your career & become a data-driven leader with Sabik.in
- Introduction to Jupyter Notebook
- Getting Started with Data Science
- Unix Introduction
- Python Basics
- Python Introduction
- Python Data Structure: Lists and Arrays
- Python: Conditions and Branching
- Python: Functions and Methods
- Python: Objects and Classes
- Practice Questions in Python
- Introduction to NumPy
- Linear Algebra in NumPy
- Seaborn, Matplotlib
- Project 1: Satellite Image Data Analysis using NumPy
- Introduction to Pandas
- Introduction to Probability
- Probability Distributions
- Describing Distributions
- Probability Distribution with Multiple Variables
- Population and Sample
- Point Estimate
- Confidence Interval
- Hypothesis Testing
- A/B Testing
- Derivatives
- Optimization
- Gradients
- Gradient Decent
- Optimization in Neural Networks
- Newton Methods
- System of Linear Equations
- Elimination Method
- Row and Row Reduced Echelon form
- Vector Algebra
- Linear Transformation
- Determinants
- Eigen Values of Eigen Vectors
- Array
- String
- Linked List
- Searching Algorithm
- Sorting Algorithm
- Divide and Conquer Acqu
- Stack
- Queue
- Tree Data Structures
- Graph Data Structures
- Dynamic Program
- Data Acquisition
- Data Wrangling
- Data Statistical Analysis, Grouping and Correlation
- Model Development
- Model Evaluation and Refinement
- Getting started in scikit-learn with the famous iris dataset
- Training a Machine Learning Model with scikit-learn
- Comparing Machine Learning Models in scikit-learn
- Data Science Pipeline: Pandas, Seaborn, and scikit-learn
- Cross-Validation for Parameter Tuning, Model Selection, and Feature Selection
- Efficiently Searching for Optimal Tuning Parameters
- Evaluating a Classification Model: Confusion Matrix and ROC
- Basic Plotting for Data Visualisation
- Data Manipulation for Visualisation
- 1D Data Analysis: Histograms, Boxplots, and Violin Plots
- Project 2: Visualization of world GDP and carbon dioxide emission
- Project 3: Using Folium Library for Geographic Overlays
- Introduction to Power-Bi
- Data Extraction Process
- Data Transformations
- Data Modeling and DAX
- Data Visualization with Analytics
- Power-Bi, Q&A & Data Insights
- Simple Linear Regression
- Multiple Linear Regression
- Non-Linear Regression
- Regression Methods
- Ridge Regression and Lasso Regression
- Linear Regression and Decision Tree Regression
- Random Forest Regression
- Logistic Regression
- Decision Tree Classification
- Random Forest Classification
- Boosting Algorithms
- Bagging
- K- Nearest Neighbours Classification
- Naive Bayes Classification
- K-Means Clustering
- Hierarchical Clustering
- K-Means and Hierarchical Clustering on the same dataset
- Density-Based Spatial Clustering of Applications with Noise (DB-SCAN) Support Vector Machines & Regression
- Principal Component Analysis (PCA)
- Applying Principal Component Analysis on Handwritten Digits Dataset
- Market Basket Analysis
- Evaluate the speed, runtime and memory dependencies of algorithmic models Parallel computing systems such as SISD (Single Instruction Single Data Stream), SIMD (Single Instruction Multiple Data Streams), MISD (MultipleInstructions Single Data Stream), MIMD (Multiple Instructions Multiple DataStreams)
- How to use coding tools
- Create, review and execute unit test cases Corrective and Preventive actions for problems and defects can improve future designs
- Measure and Optimize performance of algorithm
- Deployment of the Models
KEY HIGHLIGHTS
Comprehensive Data Science Fundamentals
Get a grip on the basics of data science, digging into numbers to make computers learn things. All with some experts.
Collaborative Learning Environment
Jump into a friendly and lively place where working together makes learning fun and easier!
Real-World Project Experience
Learn real skills by working on actual projects. You’ll face the same problems that folks do in real jobs.
Industry-Relevant Case Studies
Use real-life examples to use your data science know-how. Solve the problems that people face in industries today.
Key Features
- Comprehensive curriculum: You'll dive into all sorts of topics, like data analysis, programming, & advanced machine learning. (It’s such a combo for a well-rounded education).
- Career Counseling: Receive advice & strategies for career growth, including tips for your resume and interview prep.
- Hands-On Experience: Work on real-world projects and case studies. These will give you practical skills and help with problem-solving.
- Placement Assistance: Dedicated support will connect you with job opportunities and networking events in the industry.
- Attention: Get guidance & support from expert teachers who focus on YOUR learning needs & goals.
- State-of-the-Art Lab Facilities: Use cutting-edge lab tools both on-campus & online for an awesome learning experience.!
Skills Covered
- Python, Probability & Statistics, Calculus, Linear Algebra, Data Science Methodology
- DSA (Data Structure Algorithms), Data Visualization, Tools: Power-BI / Tableau
- Database: SQL, Machine Learning, Algorithm Design & Analysis, Deep Learning, NLP (Natural Language Processing)
- Neural Networks, LLM'S, ML Ops, Generative - AI, Prompt Engineering with ChatGPT-4
Eligibility
- Any Degree - B. Tech, BSc, B.Com, BBA, etc.
- No prior coding knowledge is required.
- All IT & Non-IT Branches - CSE, EEE, Civil, Mech, Bio, etc.
- No CGPA cut-off. Career gap is not a barrier.
Certification
Sabik's Data Analytics Certification Process:
Sabik Academy offers a certification to students who successfully complete the Data Analytics training program. This certificate validates their skills and knowledge acquired during the course. It serves as a valuable credential for career advancement in the fields of data science, artificial intelligence, and machine learning. The certification will be issued within one week after the training period ends. To qualify for the certificate, students must ensure timely completion of all required projects and assignments. This process guarantees that graduates have gained practical experience and demonstrated competence in applying their learning effectively.
FAQs
To sign up for a Data Science course, you usually need a bachelor’s degree in a related. This could be computer science, mathematics, statistics, or engineering It’s super important to have a solid background in math, especially in probability & statistics. You should also know your way around databases and be good with computer languages like R or Python. Some schools might want you to have some with machine learning or data analysis tech too.
Plus, being good at critical thinking and solving problems is good (it’s pretty much essential!). And having a strong desire to make choices based on evidence. That’s a big plus!
Yes, Data Science is a fantastic field for freshers! There are so many opportunities, & it’s in demand across lots of different industries. Freshers with a good handle on math, programming, & analytical skills can do well here. You see (and this is important), many entry-level roles, like Data Analyst or Junior Data Scientist, open doors to grow in this field.
The mix of different subjects in Data Science helps freshers learn and USE skills in real-world situations. That makes it exciting AND rewarding. Plus, there’s amazing growth potential!
Yes, Data Science is seen as safe for the future. The demand for data-driven choices is rising in many fields, so Data Science is a critical & growing area. With the constant rise of big data, artificial intelligence, and machine learning, skilled data scientists are needed.
Data Science gives you strong job security, lots of career options, and the chance to earn well. As businesses use more data to compete, Data Science will stay an important and future-proof career path. So, if you’re thinking about it, it’s a solid choice for your future.
Freshers in data science can dive into all sorts of careers. One might become a Data Analyst. They could also for a Junior Data Scientist spot. Maybe even work as a Business Intelligence Analyst.
These jobs usually mean cleaning data, doing analysis & making visualizations. It’s a great start for bigger roles later on.
But wait, there’s more! Freshers can also work as Data Engineers. This means helping with data infrastructure and Making pipelines (super cool stuff).
Internships? Entry-level positions? Yep, they give hands-on experience, too! Tons of learning & growing. It’s the path to big things like machine learning, AI, or even more data engineering fun. This is the beginning of an awesome career journey.
Freshers stepping into Data Science in India can expect a starting pay between ₹4,00,000 and ₹8,00,000 per year. Of course, this number can change. It depends on where you are, which company you join & what skills and education you have.
In big tech cities like Bengaluru or Hyderabad? Well, your starting salary could be on the higher side. Cool, right?
As you gather more experience and grow (get better) in your job, your earnings will likely go up too. Yes, there are plenty of chances for bigger paychecks as you climb the ladder in the data science world.
