Nex-G Innovations

The objective of the Certified Data Analytics Professional Training is to equip participants with comprehensive knowledge and hands-on skills in key data analytics tools and techniques.

The training covers Power BI, SAS, Tableau, Python, and SQL, enabling participants to clean, transform, and analyze data effectively. It focuses on building proficiency in data visualization, predictive analytics, machine learning, and model evaluation. By the end of the program, participants will be able to solve real-world business problems, create data-driven reports and dashboards, and complete an end-to-end analytics project, thereby preparing them for roles in the growing field of data analytics.

Code: NES_SK_22969

Duration: 80 hrs / 8 weeks / Customized

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

+91-8826900551

• Overview of Data Analytics: Importance, types, and applications
• Analytics Tools Introduction: Power BI, SAS, Tableau, Python, and SQL overview
• Hands-On: Setting up and installing analytics tools

• Types of Data: Structured, unstructured, semi-structured
• Data Sources: Databases, APIs, CSV files, and web scraping
• Hands-On: Importing data from different sources into Python, Power BI, and Tableau

• Data Cleaning Basics: Handling missing data, removing duplicates, and normalizing data
• Power BI Data Modeling: Cleaning and transforming data
• Hands-On: Data cleaning using Python (Pandas), Power BI, and Tableau

• Basic Data Exploration: Descriptive statistics, visualizations, and understanding distributions
• Using Python for Exploration: Pandas and Matplotlib for data visualization
• Hands-On: Data exploration using Python and Power BI

• Introduction to SQL: Writing queries, basic SELECT, WHERE, and JOIN operations
• Advanced SQL Concepts: GROUP BY, HAVING, and aggregate functions
• Hands-On: Writing SQL queries to retrieve, aggregate, and manipulate data

• Data Transformation Techniques: Normalization, scaling, and encoding
• SQL Aggregations: Advanced queries for data transformation and summarization
• Hands-On: Transforming data using SQL, Python, and Power BI

• Tableau Basics: Creating basic visualizations and dashboards
• Advanced Tableau Features: LOD expressions, calculated fields, and interactivity
• Hands-On: Building dashboards in Tableau

• Power BI Basics: Building simple reports and dashboards
• Advanced Power BI: Power Query and DAX for data transformation and modeling
• Hands-On: Designing dashboards and reports in Power BI

• Statistical Methods: Descriptive statistics, inferential statistics, hypothesis testing
• SAS for Statistical Analysis: Basic statistics functions in SAS
• Hands-On: Statistical analysis in SAS

• Introduction to Predictive Modeling: Types of models, regression, and classification
• Building Models in Python: Using Scikit-learn for regression and classification
• Hands-On: Building and evaluating predictive models in Python

• Advanced SQL Techniques: Subqueries, window functions, and complex joins
• Database Design and Management: Best practices for optimizing data storage
• Hands-On: Writing complex SQL queries and performing database optimizations

• Time Series Concepts: Identifying trends, seasonality, and noise
• Time Series in Python: Analyzing time series data using Pandas and statsmodels
• Hands-On: Time series forecasting in Python

• Introduction to Machine Learning: Supervised vs unsupervised learning
• Basic Algorithms: Linear regression, k-NN, and decision trees
• Hands-On: Implementing machine learning models in Python

• Model Evaluation Metrics: Accuracy, precision, recall, F1-score
• Hyperparameter Tuning: Optimizing model performance using GridSearchCV
• Hands-On: Model evaluation and hyperparameter tuning in Python

• Industry Use Cases: Analytics applications in finance, healthcare, retail, etc.
• Capstone Project - Complete an end-to-end analytics project using Power BI, SAS, Tableau, Python, and SQL