Data Science Courses In Kochi
Data Science Training In Kochi
Upgrade your Data Science Skillset with our Data Analyst courses in Kochi!
Trained 15000+ Students | Course duration: 40 hours | Real-time Project Execution | Certification exam after course completion | Basic to advanced level learning |
Key Features
Course Duration : 8 Weeks
Live Projects : 1
Online Live Training
EMI Option Available
Certification & Job Assistance
24 x 7 Lifetime Support
Our Industry Expert Trainer
We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios.
The Global Certified Trainers are Excellent in knowledge and highly professionals.
The Trainers follow the Project-Based Learning Method in the Interactive sessions.
Overview of Data Science Training Course in Kochi
Unlock the potential of data with our Data Science courses in Kochi, designed for aspiring data professionals. Our data science training in Kochi offers comprehensive lessons on key data science concepts, tools, and techniques to help you master data analysis, machine learning, and data visualization. Learn from industry experts and gain practical experience to solve real-world problems.
Our data science courses in Kochi cater to individuals with various backgrounds, from beginners to advanced learners. Whether you’re looking to become a data scientist or improve your existing data skills, our data science training in Kochi provides in-depth knowledge and hands-on projects to build your expertise.
Enroll now in the best data science training in Kochi and open up new career opportunities in the rapidly growing field of data science.
Data Science Course features
- Live Sessions
- Mocks, Assignments, & Tests
- Job Assistance
- 24/7 Lifetime Technical Support
- 10+ years of experience Proficient
- Real-time project experience
- Flexible Timings
Prerequisites
Basic knowledge of Python programming language, SQL, and files (MS Excel, CSV, etc.) with knowledge about algebra and geometry.
Course Duration
40 hours, i.e., 8-9 weeks approx.
Who all can apply for this course?
- Career switch Developers
- Candidates willing to start their career in Data Science or data analytics field
- Machine Learning or Hadoop background developers
- Data Analysts
- Business Analysts
What roles does a Data Scientist play?
Data Scientist
Develop high-quality applications along with designing and implementing scalable codes.
Analytics and Insights Analyst
Once the data has been investigated for reported errors, develop solutions for fixing quality issues.
AI & ML Engineer
Integrate Machine Learning models into web apps and deploy models in SageMaker by using Lambda functions and API Gateway.
Data Engineer & Data Analyst
Cleaning and transforming the data, analyzing the outcomes, and presenting the insights in reports and dashboards are all part of the process.
Junior Data Scientist
Utilize advanced statistical tools and techniques to analyze operating behavior. Design algorithms that include both prescriptive methods and descriptive methods.
Applied Scientist
Machine Learning models are designed and developed to derive intelligence for business products.
More employers are seeking data science professionals than ever before. Organizations are seeking data-driven insights to maintain their competitiveness, which causes the demand for data scientists to grow. Many companies, including those in the technology industry, consider this skill a “high-demand skill”.
The number of data scientist openings has been steadily increasing, with more than 3,200 at the end of every month. Big Data is a valuable tool that companies thrive to use to make good business decisions, as they realize its value.
Data Science Professionals are in Demand
1. Data management has become a challenge for companies
Every day, companies generate staggering amounts of data. In other words, every company now has a mountain of data. However, they are unsure how to use it. This data volume requires people with expertise in Data Science to organize it, analyze it, and draw meaningful insights from it.
2. Lack of skilled resources
Demand for these jobs, especially for Data Scientists, is on the rise, but these professionals are in short supply.” LinkedIn reported in August 2018 that there are more than 150,000 Americans without data science skills. This supply-demand gap will be limited by the number of aspiring data scientists who are entering the job market.
3. Multi-factors are hard to find
Professionals in the field of data science are generally expected to know at least one programming language – Python and R are the most common. As well as having experience with tools like Hadoop, Spark, NoSQL, statistical modeling, machine learning, and programming, data science professionals are expected to have training in these areas as well.
The demand for skills such as SQL, Apache Spark, and relational and NoSQL database systems is high in addition to statistical and machine learning modeling. This skill set is typically hard to find in a single person.
4. Barriers to entry for other professionals
Generally, data scientists have a degree in mathematics, statistics, computer science, engineering, or a related field, but there are also some with degrees in business, economics, or social sciences. It may be difficult for individuals without a foundation in mathematics/computers, but they can upskill themselves by taking online courses.
5. Excellent pay
The salaries for Data Scientists have increased due to the increased demand for the position and other data science jobs. This is currently the highest-paying position within the industry. Data scientists and analysts typically earn more than $62,000 per year in the United States, says Glassdoor.
The experience plays a considerable role in determining the pay in India. It is possible to earn as much as 19 lacs per year with the right skillset.
6. A Plethora of Roles
An integrated data science course in Kochi combines statistics, machine learning, data analysis, and computer programming. Data Scientists, Data Analysts, Data Architects, Business Analysts, Data Engineers, Database Administrators, Statisticians, Data, and Analytics Managers are in high demand. Among the most sought-after positions in data science are those of data scientists, who earn among the highest salaries. Until you expand the field to include positions like research engineers and machine learning engineers, it may not be wise to bet against data science as a career move in the end.
Skills Required
- No Prerequisites for Data Science certification training
- Basic knowledge of SQL is advantageous
Data Science Course Syllabus
Decade Years Legacy of Excellence | Multiple Cities | Manifold Campuses | Global Career Offers
- Fundamentals of Data Science and Machine Learning
- Introduction to Data Science
- Need of Data Science
- BigData and Data Science’
- Data Science and machine learning
- Data Science Life Cycle
- Data Science Platform
- Data Science Use Cases
- Skill Required for Data Science
- Mathematics For Data Science
- Linear Algebra
- Vectors
- Matrices
- Optimization
- Theory Of optimization
- Gradients Descent
- Introduction to Statistics
- Descriptive vs. Inferential Statistics
- Types of data
- Measures of central tendency and dispersion
- Hypothesis & inferences
- Hypothesis Testing
- Confidence Interval
- Central Limit Theorem
- Probability and Probability Distributions
- Probability Theory
- Conditional Probability
- Data Distribution
- Distribution Functions
- Normal Distribution
- Binomial Distribution
- An Introduction to Python
- Why Python , its Unique Feature and where to use it?
- Python environment Setup/shell
- Installing Anaconda
- Understanding the Jupyter notebook
- Python Identifiers, Keywords
- Discussion about installed module s and packages
- Conditional Statement ,Loops and File Handling
- Python Data Types and Variable
- Condition and Loops in Python
- Decorators
- Python Modules & Packages
- Python Files and Directories manipulations
- Use various files and directory functions for OS operations
- Python Core Objects and Functions
- Built in modules (Library Functions)
- Numeric and Math’s Module
- String/List/Dictionaries/Tuple
- Complex Data structures in Python
- Python built in function
- Python user defined functions
4. Introduction to NumPy
- Array Operations
- Arrays Functions
- Array Mathematics
- Array Manipulation
- Array I/O
- Importing Files with Numpy
5. Data Manipulation with Pandas
- Data Frames
- I/O
- Selection in DFs
- Retrieving in DFs
- Applying Functions
- Reshaping the DFs – Pivot
- Combining DFs
Merge
Join - Data Alignment
6. SciPy
- Matrices Operations
- Create matrices
Inverse, Transpose, Trace, Norms , Rank etc - Matrices Decomposition
Eigen Values & vectors
SVDs
7. MatPlotLib & Seaborn
- Basics of Plotting
- Plots Generation
- Customization
- Store Plots
8. SciKit Learn
- Basics
- Data Loading
- Train/Test Data generation
- Preprocessing
- Generate Model
- Evaluate Models
9. Descriptive Statistics
. Data understanding
- Observations, variables, and data matrices
- Types of variables
- Measures of Central Tendency
- Arithmetic Mean / Average
- Merits & Demerits of Arithmetic Mean and Mode
- Merits & Demerits of Mode and Median
- Merits & Demerits of Median Variance
10. Probability Basics
- Notation and Terminology
- Unions and Intersections
- Conditional Probability and Independence
11. Probability Distributions
- Random Variable
- Probability Distributions
- Probability Mass Function
- Parameters vs. Statistics
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Standard Normal Distribution
- Central Limit Theorem
- Cumulative Distribution function
12. Tests of Hypothesis
- Large Sample Test
- Small Sample Test
- One Sample: Testing Population Mean
- Hypothesis in One Sample z-test
- Two Sample: Testing Population Mean
- One Sample t-test – Two Sample t-test
- Paired t-test
- Hypothesis in Paired Samples t-test
- Chi-Square test
13. Data Analysis
- Case study- Netflix
- Deep analysis on Netflix data
- Exploratory Data Analysis
- Data Exploration
- Missing Value handling
- Outliers Handling
- Feature Engineering
- Feature Selection
- Importance of Feature Selection in Machine Learning
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Machine Learning: Supervised Algorithms Classification
- Introduction to Machine Learning
- Logistic Regression
- Naïve Bays Algorithm
- K-Nearest Neighbor Algorithm
- Decision Tress
- SingleTree
- Random Forest
- Support Vector Machines
- Model Ensemble
- Model Evaluation and performance
- K-Fold Cross Validation
- ROC, AUC etc…
- Hyper parameter tuning
- Regression
- classification
- Machine Learning: Regression
- Simple Linear Regression
- Multiple Linear Regression
- Decision Tree and Random Forest Regression
- Machine Learning: Unsupervised Learning Algorithms
- Similarity Measures
- Cluster Analysis and Similarity Measures
- Ensemble algorithms
- Bagging
- Boosting
- Voting
- Stacking
- K-means Clustering
- Hierarchical Clustering
- Principal Components Analysis
- Association Rules Mining & Market Basket Analysis
- Machine Learning end to end Project blueprint
- Case study on real data after each model.
- Regression predictive modeling – E-commerce
- Classification predictive modeling – Binary Classification
- Case study on Binary Classification – Bank Marketing
- Case study on Sales Forecasting and market analysis
- Widespread coverage for each Topic
- Various Approaches to Solve Data Science Problem
- Pros and Cons of Various Algorithms and approaches
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Who can apply for the course?
- Graduates who want to become AWS Cloud Practitioner
- BE / BTech / MCA passed aspirants to make their career in Cloud Computing.
- IT-Professionals who want to get career in AWS Cloud Computing.
- Professionals from non-IT bkg, and want to establish in IT.
- Candidate who would like to restart their career after a gap.
- Web Designers for next level of their career.
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