Data Science And Analytics Training Course

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Data Science And Analytics Training Course

Course Detail
Course Level:	Beginner to Advanced
Course Duration:	4 Months \| 8 Months
Training Days:	Monday to Friday
Training Time:	4 hours / Day \| Regular Office Time
Course Mode:	IN-class (Offline) at our premises
Course Type:	JOB oriented training
Course Start On:	On Registration \| Admission
Class Size:	1 to 1 \| No Groups\| No Batch

COURSE BENEFITS

Considering is your last training: We assure for knowledge, so once your get job then your training will end.
Know your skills:Choose/Suggested a technology what you can do best.
Authenticate your skills: Entire course is on industrial practice so awarded with experience latter on placement.
Be highest paid fresher:We invented a unique model to get the job with highest starting salary, if you get good offer then US, you can join to them.
We don’t bind your ability: No specific course content, learn as much as you can, beyond the topics it helps to become logically sound.

Curriculum
Reviews

Introduction to Data Science

What is Data Science?

Importance of Data Science in Today’s World.

Applications of Data Science in Various Industries.

Data Science vs Data Analytics vs Machine Learning vs AI.

Career Opportunities in Data Science.

This beginner-friendly data science course is designed to help any graduate freshers build a strong foundation in data analysis, machine learning, and data visualization.

Python for Data Science

Introduction to Python and Jupyter Notebook.

Python Basics (Variables, Data Types, Operators).

Control Flow (Loops, Conditional Statements).

Functions and Modules.

Working with Python Libraries

NumPy for Numerical Computing

Pandas for Data Manipulation

Matplotlib & Seaborn for Data Visualization.

Data Handling and Preprocessing

Types of Data: Structured vs Unstructured.

Importing and Exporting Data (CSV, Excel, JSON).

Data Cleaning and Handling Missing Values.

Data Transformation and Feature Engineering.

Handling Outliers and Data Normalization.

Exploratory Data Analysis (EDA)

Importance of EDA in Data Science.

Univariate and Bivariate Analysis.

Data Visualization Techniques.

Correlation and Covariance Analysis.

Detecting and Handling Outliers.

Introduction to Statistics & Probability

Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation).

Probability Distributions (Normal, Binomial, Poisson).

Hypothesis Testing & Confidence Intervals.

Correlation vs Causation.

Introduction to Machine Learning

What is Machine Learning?

Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning).

Introduction to Regression and Classification.

Introduction to Clustering Algorithms.

Hands-on Implementation Using Scikit-Learn.

Working with Real-World Datasets

Understanding Business Problems through Data.

End-to-End Data Science Project Workflow.

Data Collection and Cleaning Best Practices.

Model Building and Evaluation.

SQL for Data Science

Introduction to Databases and SQL.

Writing SQL Queries (SELECT, JOIN, GROUP BY, WHERE, HAVING).

Data Aggregation and Filtering.

Using SQL for Data Analysis.

Data Visualization & Dashboarding

Advanced Matplotlib & Seaborn for Visualization.

Building Interactive Dashboards with Power BI/Tableau (optional).

Storytelling with Data.

Target Audience and Prerequisites are,

This structured course will equip for any graduate with the necessary skills to start a career in data science. this Course is customize based on your requirement.

Fresh Graduates: Individuals who have recently completed their education and are looking to enter the field of software development or DS.

Junior Developers/Admin: Developers with limited experience who want to enhance their skills with containerization technologies.

Career Switchers: Professionals from different fields who are transitioning into tech and want to understand the basics of Docker.

Students: Current students in computer science or related fields who wish to gain practical skills in Docker.

LEARN WHICH BEST SUITS YOU

No limits on learning, no limits on duration, no limits on salary, no limits on interviews, learn as much as you can & get ready for your first job.

4 MONTHS TRAINING(CODE :- PTP 4)

4 months training duration
Monday to Friday (04 hours / Day)
Only practical based training
Individual 1 to 1 training
Professional developers as trainer
Stipend provide based on performance
Confirmed job – on-job training program

8 MONTHS TRAINING(CODE :- PTP 8)

8 months training duration
Monday to Friday (Regular office time)
Live & Direct work with team.
Stipend during training, Attractive salary offer.
+Unlimited placement, Dual job opportunity.
Get your first job offer on the day of joining.
IN as fresher OUT as experienced professional developer

XCEL PTP

^tm

professional it trainnig program

Data Science And Analytics Training Course

COURSE BENEFITS

Considering is your last training: We assure for knowledge, so once your get job then your training will end.

Know your skills:Choose/Suggested a technology what you can do best.

Authenticate your skills: Entire course is on industrial practice so awarded with experience latter on placement.

Be highest paid fresher:We invented a unique model to get the job with highest starting salary, if you get good offer then US, you can join to them.

We don’t bind your ability: No specific course content, learn as much as you can, beyond the topics it helps to become logically sound.

Introduction to Data Science

Python for Data Science

Data Handling and Preprocessing

Exploratory Data Analysis (EDA)

Introduction to Statistics & Probability

Introduction to Machine Learning

Working with Real-World Datasets

SQL for Data Science

Data Visualization & Dashboarding

Target Audience and Prerequisites are,

Course Information

SELECT BY CATEGORY

LEARN WHICH BEST SUITS YOU

4 MONTHS TRAINING(CODE :- PTP 4)

8 MONTHS TRAINING(CODE :- PTP 8)

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