DATA ANALYTICS-BEGINNER
OBJECTIVES
The objective of this training program is to provide an introduction to data analytics for IIOT data and equip learners with the knowledge and skills needed to collect, analyze, and visualize IIOT data using various data analytics tools and techniques.
OUTCOMES
Understand the basics of IIOT and its role in modern industry
Collect and prepare IIOT data for analysis using various techniques and tools
Perform exploratory data analysis and identify patterns and trends in IIOT data
Develop predictive models using machine learning algorithms for IIOT data
Perform time series analysis and forecasting on IIOT data
Implement real-time data processing and analytics on IIOT data
Understand the security and privacy concerns of IIOT data and techniques to address them
SCOPE
- IIOT Data Analyst
- IIOT Data Engineer
- IIOT Data Scientist
- IIOT Analytics Consultant
PROJECTS
Develop a predictive maintenance model for IIOT data
Analyze the energy consumption of a manufacturing plant using IIOT data
Develop a real-time dashboard for monitoring IIOT data
Perform a time series analysis on IIOT data to identify production trends
Build a machine learning model to predict the failure of a machine based on IIOT data
DATA ANALYTICS-INTERMEDIATE
OBJECTIVES
The objective of this training program is to provide participants with an in-depth understanding of advance data analytics and its various components, including data collection strategies, high volume and velocity of data, different types of data cleaning, noise removal, data integration, data transformation, data analytics and predictive modeling, data visualization, and histograms. Participants will also gain practical experience in applying these techniques to real-world data sets and projects.
OUTCOMES
Understand the fundamentals of data analytics and its various components.
Identify and evaluate data sources and collection strategies.
Clean and preprocess data using a variety of techniques.
Apply noise removal techniques to enhance data quality.
Integrate and transform data to meet specific business requirements.
Analyze and model data using advanced predictive modeling techniques.
Visualize data using a variety of techniques and tools.
Use histograms to analyze data distributions and make data-driven decisions.
Work effectively with large data sets and real-time data processing
SCOPE
Participants who complete this training program will be equipped with the knowledge and skills necessary to pursue a career in data analytics or related fields. Job roles that participants can consider include
- Business Analysts
- Data Engineers
- Data Visualization Specialists
- Data Analyst
- Data Scientist
PROJECTS
Predicting Customer Churn: Use predictive modeling techniques to identify factors that contribute to customer churn in a subscription-based business model. Develop a model that can accurately predict which customers are at risk of churning, and use these insights to implement targeted retention strategies.
Improving Supply Chain Efficiency: Analyze supply chain data to identify bottlenecks, optimize delivery routes, and reduce transportation costs. Use data integration and transformation techniques to merge and preprocess data from multiple sources, and develop visualizations that provide insights into supply chain performance.
DATA ANALYTIC- ADVANCE
OBJECTIVES
The objective of this training program in Data Analytics for Business and Finance is to equip participants with the knowledge and skills required to analyze large data sets and make data-driven decisions in business and finance. The program aims to provide participants with hands-on experience with various data analytics tools and techniques to prepare them for a career in data analytics in the business and finance sector.
OUTCOMES
Understand the fundamentals of data analytics and its applications in business and finance.
Collect, pre-process, and analyze large data sets using various data analytics tools and techniques.
Conduct exploratory data analysis, descriptive analytics, inferential analytics, regression analysis, time series analysis, and predictive analytics.
Interpret and communicate insights and findings from data to stakeholders.
Apply data analytics techniques to real-world business and finance problems.
SCOPE
The job scope for a data analyst in the business and finance sector is vast. Data analysts in this field are responsible for collecting, pre-processing, analyzing, and interpreting large data sets to identify patterns, trends, and insights that can be used to drive business decisions. They work in various industries, including banking, insurance, investment, accounting, and financial consulting. The job roles for a data analyst in this field include Financial Analyst, Business Analyst, Data Analyst, Investment Analyst, and Risk Analyst.
PROJECTS
The training program may include hands-on projects to provide participants with practical experience in using data analytics tools and techniques. Some of the project ideas for this program are:
Analyzing customer behavior data to identify potential opportunities for cross-selling and up-selling in the banking industry.
Conducting a time series analysis of stock prices to predict future trends in the stock market.
Conducting a predictive analysis of credit card fraud transactions to identify fraudulent activities and prevent losses.
Analyzing sales data to identify the most profitable product lines and regions for a retail company.
Real-Time Sentiment Analysis: Collect and analyze social media data in real-time to track sentiment around a particular brand, product, or topic. Use natural language processing techniques to classify social media posts as positive, negative, or neutral, and develop visualizations that provide real-time insights into public sentiment.
Fraud Detection in Financial Transactions: Develop a fraud detection model using machine learning techniques to identify potentially fraudulent transactions in financial data. Use data cleaning and transformation techniques to preprocess the data and improve model accuracy, and develop visualizations that provide insights into potential fraud patterns.