Overview of Analytics Course in Salem

Salem, a city located in Tamil Nadu, India, has recently seen an increase in the demand for Analytics courses. Analytics, also called data analysis, is defined as the process of examining data to draw conclusions about information that it contains. Analytics courses in Salem are designed to provide students with the skills and knowledge they need to become data analysts and help businesses grow through smart data-driven decision making. One of the popular Analytics courses offered in Salem is a Postgraduate Diploma in Data Science. The course is usually a one-year program that teaches students the basics of data science, including statistical analysis, data visualization, machine learning, and programming languages like R and Python. Another popular Analytics course in Salem is the Bachelor of Technology in Data Science. The four-year undergraduate program focuses on the foundations of data science, including mathematics, statistics, programming, and data analysis. Students learn how to process, store and analyze huge volumes of data using advanced tools and techniques in this course. Salem also has specialized courses in Business Analytics, which blends elements of statistics, data analysis, and business management. The program teaches students the skills they need to work in fields such as marketing, finance, and sales. It helps participants tackle business challenges and make data-driven decisions that drive growth and innovation. In addition to academic courses, Salem has numerous training programs in analytics. For instance, some of the institutes in Salem offer short-term online courses in Analytics, which enable students to learn at their own pace. The demand for Analytics courses in Salem continues to grow, as organizations recognize the importance of data analytics and invest in upskilling their employees. According to a recent study, Analytics roles are projected to increase by 33% between 2020 and 2025, making it one of the fastest-growing industries in the world. This growing trend has spurred Salem's educational institutions to broaden their Analytics course offerings for both undergraduate and graduate students. Salem offers an excellent learning environment for Analytics students. The city's educational institutions have a reputation for providing quality education with a focus on practical learning. This approach allows graduates to apply the concepts they learn in class to real-world business challenges, making them more competitive in the job market. In conclusion, Salem is an excellent destination for aspiring data analysts and data scientists. The city's Analytics courses equip students with requisite skills, as they prepare to embark on a career in the ever-evolving data-driven business landscape. The field of analytics is bound to grow, and Salem's educational institutions are poised to continue providing the requisite skills to up-and-coming analysts.

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Course Duration

2

Internship

Yes

Live Training

Yes

Career Assistance

Yes
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Skillfloor Course Training Process
Skillfloor Course Training Process

Internship Certificate

internship certificate

Completing this Analytics Course certifies your proficiency in data analysis, statistical methods, and essential analytics tools. This certification shows you can transform raw data into actionable insights, improving decision-making in various business contexts. It indicates your understanding of data visualization, predictive modelling, and data-driven strategy development, providing you with the skills to excel in analytics. This credential reflects your dedication to continuous learning and commitment to leveraging data for strategic advantage.

Analytics Tools Covered

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Why Choose SKILLFLOOR for Analytics in Salem?

Why Course Training in Skillfloor

Syllabus

- Overview of data analysis and its importance in business

- Types of analytics: Descriptive, Predictive, Prescriptive

- Role of data in decision-making processes

- Introduction to common tools: Tableau, PowerBI, Excel

- Ethical considerations in data collection and analysis

- Data sources: Primary and secondary data

- Data collection methods (surveys, web scraping, databases)

- Data cleaning techniques (handling missing values, outliers)

- Data transformation and feature engineering

- Data storage concepts (structured vs. unstructured data)

- Descriptive statistics: Mean, median, mode

- Data visualization basics (histograms, scatter plots)

- Identifying data patterns and trends

- Outlier detection and handling methods

- Correlation and causation analysis

- Inferential statistics and probability theory

- Hypothesis testing (t-tests, chi-square tests, ANOVA)

- Measures of central tendency and variability

- Confidence intervals and margin of error

- Regression analysis: Linear and logistic regression

- Principles of effective data visualization

- Types of charts and their uses (bar, line, pie, heatmaps)

- Designing dashboards for different audiences

- Interactive visualization techniques

- Data storytelling for impactful presentations

- Time series analysis and forecasting methods

- Clustering and segmentation analysis

- Decision trees and classification techniques

- Introduction to machine learning in business analytics

- Model evaluation and selection

- Basics of SQL for data manipulation

- Creating databases and relationships

- Aggregating data with SQL (GROUP BY, JOIN)

- Data modeling for business intelligence (star and snowflake schemas)

- Case study: Building a business model with SQL

- Connecting and preparing data in Tableau

- Creating basic visualizations (charts, maps)

- Advanced Tableau functions (LOD calculations, table calculations)

- Building interactive dashboards and stories

- Publishing and sharing visualizations on Tableau Server/Online

- Introduction to PowerBI workspace and components

- Data import and transformation with Power Query

- Data modeling and relationships in PowerBI

- Creating and customizing visualizations

- Publishing and collaborating on PowerBI Service

- Selecting a real-world dataset for analysis

- Defining business questions and objectives

- Conducting data analysis and visualization

- Presenting findings in a comprehensive dashboard

- Peer review and feedback on project

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