Nex-G Innovations

The objective of the Certified Data Science Professional Training is to equip participants with a comprehensive understanding of data science principles and tools, enabling them to analyze, interpret, and derive actionable insights from data.

This program focuses on mastering core technologies such as Python, R, SQL, and key libraries like Pandas, NumPy, and Scikit-learn, alongside advanced techniques in machine learning, data visualization, and statistical analysis. Participants will develop the skills to handle real-world datasets, work with big data platforms, and implement predictive models, preparing them for data-driven decision-making and problem-solving across industries.

Code: NES_SK_1325

Duration: 80 hrs / 8 weeks / Customized

Mode: Corporate (Instructor Led - Online / Offline / Onsite), Individual (Instructor Led - Online / Offline)

+91-8826900551

• What is Data Science?
• Importance and Applications
• Data Science Lifecycle
• Roles in Data Science: Data Engineer, Data Analyst, Data Scientist

• Understanding Data Types and Formats
• Data Collection Methods
• Handling Missing Data
• Data Preprocessing Techniques

• Data Manipulation with Pandas (Python) or dplyr (R)
• Data Visualization Basics
• Plotting with Matplotlib, Seaborn (Python) or ggplot2 (R)
• Dashboarding with Tableau/Power BI

• Descriptive Statistics
• Identifying Trends and Patterns
• Detecting Outliers
• Hypothesis Testing

• Introduction to Machine Learning
• Supervised vs. Unsupervised Learning
• Overview of Algorithms (Linear Regression, Decision Trees, K-Means, etc.)
• Model Evaluation Metrics (Accuracy, Precision, Recall, etc.)

• Feature Engineering and Selection
• Ensemble Methods (Random Forest, Gradient Boosting)
• Neural Networks Overview
• Model Optimization Techniques (Grid Search, Hyperparameter Tuning)

• Overview of Big Data Technologies (Hadoop, Spark)
• Introduction to Cloud Platforms (AWS, Azure, Google Cloud)
• Using Cloud Services for Data Science (e.g., AWS SageMaker)

• Hands-On Project: Building an End-to-End Data Science Solution
• Industry-Specific Case Studies (e.g., Healthcare, Finance, Retail)