Certified Data Science Professional

Master Data Science — Python • Statistics • Machine Learning • Data Visualization

Data Science is transforming how organizations analyze information, identify patterns, predict outcomes, and make data-driven decisions. This program helps learners develop practical skills in Python programming, statistics, data analysis, machine learning, and data visualization to solve real-world business problems.

Learn how to collect, clean, process, and analyze datasets, build predictive models, and communicate meaningful insights through hands-on exercises and industry-relevant projects. Develop a strong foundation in data science and explore how analytical techniques and machine learning can support smarter business decisions.

  • Comprehensive Data Science Curriculum
  • Hands-on Training with Real-World Datasets
  • Practical Projects in Data Analysis, Machine Learning, and Visualization
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Flexible
Course Duration
Advanced
Course Level
Online
Course Delivery
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Program Highlights

The Data Science Certification Training helps learners develop practical expertise in data analysis, Python programming, statistics, machine learning, and data visualization. Gain experience working with datasets, identifying trends, developing predictive models, and presenting analytical findings.

💻 Instructor-Led Training and Practical Learning
🎥 Recorded Video Sessions for Flexible Learning
👨‍🏫 Industry Expert Guidance and Learner Support
🧪 Hands-on Labs with Real-World Datasets
📊 Machine Learning Exercises and Data Science Projects
📄 Resume Preparation and Career Guidance

Why Choose Data Science Certification Training?

Master Data Analysis, Machine Learning, and Predictive Modeling

Build a strong foundation in data science by learning how to transform raw data into meaningful insights. Develop practical skills in statistical analysis, exploratory data analysis, machine learning, and predictive modeling to solve business and analytical challenges.

Comprehensive Curriculum
Python, Statistics, Data Analysis, Machine Learning, Data Visualization, and Predictive Modeling.
Practical Learning
Work with datasets and analytical tools to understand data patterns, test hypotheses, and develop data-driven solutions.
Hands-on Labs & Projects
Practice data preparation, exploratory analysis, feature engineering, model development, and performance evaluation.
Certification and Assessment
Build job-relevant skills and prepare for the applicable course assessment or certification, subject to the selected program.
Enroll Now Course Curriculum
Skills
Practical Learning
Data
Real-World Datasets
Data Science
Certification Program
Practical Training
Data science model and connected dataset visualization

Data Science Certification Training

The Data Science Certification Training equips learners with the technical and analytical skills required to work with data and develop data-driven solutions. Learn to process datasets, apply statistical methods, build machine learning models, and communicate insights effectively.

Explore how data science techniques can be used in business forecasting, customer analysis, fraud detection, recommendation systems, and other real-world applications. Develop a practical foundation that can support further learning in artificial intelligence, machine learning, and advanced analytics.

“Data Scientist salaries range from ₹8,00,000 to ₹25,00,000 annually depending on experience. - Glassdoor”
“Machine Learning Engineer average pay is around ₹15,00,000 per year. - LinkedIn”

Program Level

What You Will Learn

1. Introduction to Data Science
  • Data science concepts, applications, lifecycle, and analytical workflows
  • Types of data and data sources; data collection, preparation, and processing
  • Understanding business problems and analytical objectives
  • Introduction to descriptive and inferential statistics
2. Python Programming for Data Science
  • Python fundamentals, variables, data types, loops, functions, and modules
  • Lists, dictionaries, and other data structures
  • Reading and processing datasets and writing reusable Python code
  • Using Jupyter Notebook for data science workflows
3. Statistics and Mathematics for Data Science
  • Descriptive statistics, probability, mean, median, mode, variance, and standard deviation
  • Probability distributions, sampling, correlation, and regression fundamentals
  • Hypothesis testing and statistical interpretation
  • Linear algebra fundamentals relevant to machine learning
4. Data Analysis and Preprocessing
  • Data cleaning, handling missing values, duplicates, and inconsistent data
  • Data transformation, normalization, and exploratory data analysis (EDA)
  • Working with Pandas and NumPy; identifying patterns, relationships, and outliers
  • Feature selection and feature engineering fundamentals
5. Data Visualization
  • Principles of effective data visualization
  • Creating charts and graphs with Matplotlib and Seaborn
  • Visualizing distributions, relationships, and trends
  • Building analytical reports, data storytelling, and selecting visualizations for datasets
6. Machine Learning Fundamentals
  • Supervised and unsupervised learning; linear and logistic regression
  • Decision trees, random forests, classification, regression, and K-means clustering
  • Model training, testing, validation, and performance metrics
  • Overfitting, underfitting, and generalization
7. Advanced Data Science Concepts
  • Feature engineering, model optimization, cross-validation, and hyperparameter tuning
  • Introduction to ensemble learning and dimensionality reduction
  • Model interpretation, error analysis, time-series analysis, and forecasting
  • Responsible data science practices
8. Hands-on Projects and Career Preparation
  • Customer churn prediction, sales forecasting, and business performance analysis
  • Customer segmentation, house price prediction, and exploratory data analysis
  • Classification model development using real-world datasets
  • Portfolio development and interview preparation

Core Traits You’ll Build

Analytical ThinkingStatistical ReasoningProblem-SolvingPython ProgrammingCritical ThinkingData InterpretationMachine Learning

Your Analyst Style

Tools and Technologies Covered: Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, SQL, Excel, and Git (if included in the selected program).

Data Science Training and Assessment

Build practical skills in data analysis, Python programming, statistics, machine learning, and data visualization. Certification or assessment details depend on the selected program.

Data Science Certificate

Data Science Certification and Assessment

Build job-relevant data science skills and prepare for the applicable course assessment or certification, subject to the selected program. Confirm the certification provider, assessment requirements, and inclusions before enrollment.

  • Learn data analysis, statistics, Python, machine learning, and predictive modeling
  • Practice with datasets, analytical tools, and data visualization
  • Explore practical data science projects and applications
  • Confirm certification and assessment details for the selected program

Tools & Technologies Covered

Microsoft Excel

Data cleaning, formulas, pivot tables, and reporting.

SQL & Databases

Query, filter, join, and analyze structured datasets.

Python

Automate analysis and explore data using libraries.

Power BI

Create interactive dashboards and business reports.

Tableau

Visualize trends and communicate analytical findings.

Pandas

Clean, manipulate, and analyze datasets with Python.

NumPy

Perform numerical calculations and array-based analysis.

Statistics

Apply descriptive and inferential statistics to identify patterns, test hypotheses, and draw reliable conclusions from data.

GGplot

GGplot is a system for declaratively creating graphics, based on The Grammar of Graphics.

Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics.

Pycharm

Pycharm is a Python IDE that provides a comprehensive set of tools for developing Python applications.

Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Data Science Certification Training

Data Science combines programming, statistics, mathematics, and analytical techniques to extract insights from data. This training builds a foundation in Python programming, statistics, data analysis, and machine learning.

Learn to process datasets, apply statistical methods, build machine learning models, and communicate insights. Explore applications such as business forecasting, customer analysis, fraud detection, and recommendation systems.

Organizations use data to identify opportunities, solve problems, and improve performance. Data science combines programming, mathematics, statistics, and analytical thinking to develop data-driven solutions.

Work with datasets and practical case studies involving sales, customer behavior, business forecasting, and operational performance. Apply data cleaning, exploratory analysis, visualization, and machine learning techniques.

Build skills relevant to roles such as Junior Data Scientist, Data Analyst, Machine Learning Analyst, Business Intelligence Analyst, and Predictive Analytics Associate, depending on experience and technical proficiency.

Student Testimonials

Rahul Sharma

Lokesh Dandamudi

★★★★★

I had an excellent experience with Securium Academy! The courses are well-structured, up-to-date, and tailored to meet industry standards, especially in the field of cybersecurity. The instructors are highly knowledgeable and supportive, breaking down complex topics into easily understandable modules. What stood out the most was their hands-on approach with practical labs, real-world scenarios, and continuous mentoring. Whether you're a beginner or looking to advance your career, Securium Academy offers the right blend of theory and practice. Highly recommended for anyone serious about building a strong foundation in cybersecurity!

Lajin C P

★★★★★

Exam was very nice.. It was an excellent experience.. I highly recommend everyone who is highly passionate in cyber security field should attend this exam.

user

Malle Ganesh

★★★★★

My overall experience is soo Good with Securium Academy. They have excellent trainers with Good teaching Skills. Especially My trainer so good in CYBER SECURITY knowledge with excellent practical knowledge. They also providing Coupons for the certifications

user

Alka Sajeevan P

★★★★★

Happy to share that I have successfully earned the Certified Ethical Hacker (CEH) certification from EC-Council! This achievement has deepened my understanding of ethical hacking methodologies, penetration testing, and advanced cybersecurity concepts — empowering me to identify, analyze, and secure systems against evolving cyber threats.

user

Sreelakshmi Suresh

★★★★★

I recently completed the Certified Secure Computer User (CSCU) certification, and I must say it was an excellent experience! The CSCU program is well-structured and beginner-friendly, making it perfect for anyone looking to build a strong foundation in cybersecurity awareness, internet safety, and digital hygiene.

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Frequently Asked Questions

Data Science combines programming, statistics, mathematics, and analytical techniques to extract insights from data. It helps organizations understand patterns, predict outcomes, and make informed decisions using data-driven methods.

The program is suitable for students, graduates, working professionals, data analysts, business analysts, and IT professionals who want to develop skills in Python, statistics, data analysis, and machine learning.

Topics may include Data Science fundamentals, Python programming, statistics and probability, data cleaning and preprocessing, exploratory data analysis, data visualization, machine learning fundamentals, predictive modeling, model evaluation, and practical projects.

Prior programming experience is not necessarily required for a beginner-level course. Basic computer skills and an interest in mathematics and analytical problem-solving are helpful. Learners can start with Python fundamentals before progressing to data analysis and machine learning.

Python is widely used because of its libraries for data analysis, visualization, statistical computing, and machine learning. SQL can also be useful for retrieving and managing data from databases.

Depending on the final curriculum, learners may use Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, SQL, and Excel.

The proposed curriculum includes projects involving customer churn prediction, sales forecasting, customer segmentation, regression, classification, and exploratory data analysis. Confirm the final project list and hands-on training inclusions before enrollment.

Depending on prior experience, technical proficiency, and project work, learners may explore opportunities such as Junior Data Scientist, Data Analyst, Machine Learning Analyst, Business Intelligence Analyst, and Predictive Analytics Associate.

The program can help working professionals strengthen Python, statistical analysis, and machine learning skills. Confirm the available schedule and delivery format to determine whether the course fits your work commitments.

Duration and fee depend on the selected training package, delivery format, learning resources, and certification inclusions. Contact Securium Academy for current course details and payment options.

Certificate eligibility depends on the selected program. Confirm whether enrollment includes a course completion certificate, an external certification examination, or both.

Yes. Beginners can start with Python programming, basic mathematics, and descriptive statistics before progressing to data cleaning, visualization, and machine learning. Regular practice with datasets and projects helps develop practical skills.

Data Analytics generally focuses on examining existing data to understand performance, identify trends, and support decisions. Data Science often includes these activities while also emphasizing statistical modeling, machine learning, predictive analysis, and data-driven models.

The learning timeline depends on prior experience, study schedule, and curriculum depth. Beginners generally need consistent practice with Python, statistics, data analysis, and machine learning before confidently working on independent projects.

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