Data Science
Unlock the Power of Data with Our Industry-Recognized Certification
In today’s data-driven world, mastering Data Science is the key to unlocking new career opportunities. At Saras Consultancy, we bring you the Data Science Certification from Techtroma, a comprehensive program designed to equip professionals and aspiring data enthusiasts with cutting-edge skills.
Why Choose This Certification?
Industry-Aligned Curriculum – Covers Python, Machine Learning, AI, Deep Learning, SQL, Power BI, and more.
Hands-On Learning – Real-world projects, case studies, and practical applications.
Expert-Led Training – Learn from experienced Data Scientists and industry practitioners.
Certification from Techtroma – A recognized credential that enhances your professional credibility.
Placement Assistance – Get access to job opportunities in top companies.
Who Can Enroll?
- Fresh Graduates & Students looking to build a career in Data Science
- IT Professionals & Developers transitioning to Data Analytics & AI
- Business Analysts & Managers seeking data-driven decision-making skills
- Entrepreneurs & Startups leveraging data for growth
Course Highlights
Foundations of Data Science – Statistics, Probability, and Data Handling
Programming with Python & R – Data Wrangling, Visualization, and Automation
Machine Learning & AI – Supervised & Unsupervised Learning, Deep Learning, NLP
Big Data & Cloud – Hadoop, Spark, AWS, and Azure for scalable solutions
Data Visualization & Business Intelligence – Power BI, Tableau, and Data Storytelling
Capstone Project & Internship – Industry-driven projects to build a strong portfolio
Career Opportunities
After completing this certification, you can explore roles such as:
Data Scientist
Data Analyst
Business Intelligence Analyst
Machine Learning Engineer
AI Specialist
Course Curriculum
1.Introduction to Data Science.
1.1 Introduction to Data Science
1.2 What is Data Science?
1.3 Why Data Scientist?
1.4 Cases for Data Science. 1.5 Data Science Classification
2.Basic Python Programming
2.1 Introduction
2.2 Python Data Types- int, Number, Boolean
2.3 String, Tuple, List, Dictionary and set
2.4 String Operation and Functions.
2.5 Tuple operation and Functions.
2.6 List operation and Functions
2.7 Set operation and Functions.
2.8 Dictionary operation and Functions.
2.9 If-else, elif ,Statement
2.10 Loops & Break and Continue Statement.
3.Basic Statistics
3.1 Introduction Statistics.
3.2 Data Types.
3.3 Central Tendency.
3.4 Dispersion.
4.Visualizations
5.1 Basic Statistics Contents.
5.2 Datasets.
5.3 Graph designing.
5.4 Filtering dataset
5.5 Mapping a dataset
5.Machine Learning
6.1 Introduction to Machine Learning.
6.2 Overview of Machine Learning Algorithm.
6.3 Supervised Machine Learning .
6.4 Unsupervised Machine Learning.
6.5 Reinforcement Machine Learning.
6.Data Science Process.
6.1 Retrieving data.
6.2 Data Exploration.
6.3 Data Manipulation.
6.4 Data Modelling.
6.5 Data Analysis.
7.Normal Distribution.
7.1 Introduction to Normal Distribution.
7.2 Overview of Random Variable.
7.3 Overview of Probability.
7.4 Datasets.
7.5 Example of Normal Distribution.
8.Natural Language Processing
8.1 Introduction NLP.
8.2 Applications of NLP.
8.3 NLP Technology Overview.
8.4 AI/ML of NLP Programming Languages.
8.5 NLP Libraries.
8.6 Examples.
9.Linear Regression.
9.1 Introduction.
9.2 How to Retrieving data using Linear Regression.
10.Logistics Regression.
10.1 Introduction.
10.2 How to Retrieving data using Logistics Regression.
10.3 Datasets and codes.
11.Hypothesis Testing.
11.1 Introduction to Hypothesis testing.
11.2 Codes and Examples.
12.Neural Networks.
12.1 Overview of K-Means Clustering.
12.2 K-Nearest neighbors.
12.3 Approximate Nearest neighbors.
12.4 Artificial Neural Network.
Data Scientist Project Life Cycle
Phase 1: Problem Understanding & Data Preparation.
This phase focuses on defining business objectives and identifying key performance indicators (KPIs). Data is collected from various sources, cleaned, and preprocessed to ensure consistency. Exploratory Data Analysis (EDA) helps uncover patterns and relationships for model selection.
Phase 2: Model Development & Evaluation.
Machine learning models are selected and trained using a split dataset. Feature engineering and hyperparameter tuning enhance accuracy. Models are evaluated using performance metrics like accuracy, precision, and recall to finalize the best-performing model.
Phase 3: Deployment & Monitoring
The trained model is deployed via APIs, cloud platforms, or containerized solutions. Continuous monitoring ensures performance stability, addressing data drift and retraining when needed. Insights are shared through dashboards for data-driven decision-making.