Overview of Analytics Course in Thiruvananthapuram

Thiruvananthapuram, the capital city of Kerala, is a hub for education and technology in South India. With the increasing demand for skilled professionals in data analytics, many institutions in Thiruvananthapuram are offering courses in the field, catering to students and professionals seeking to enhance their skills. Data analytics is the process of analyzing and interpreting large sets of data to extract meaningful insights, trends, and patterns. With the increased availability of data across industries, there is a growing need for individuals with expertise in data analytics to help organizations make data-driven decisions. One of the benefits of pursuing an analytics course in Thiruvananthapuram is the availability of quality institutions like Indian Institute of Technology (IITM), Indian Institute of Information Technology and Management (IIITMK), and Centre for Development of Advanced Computing (CDAC), among others. These institutions offer courses in data analytics at different levels, ranging from short-term certification courses to full-time postgraduate programs. Students can choose from a range of courses, including Big Data Analytics, Business Analytics, Data Science, and Data Engineering. These courses cover various topics, including data visualization, machine learning, statistical analysis, and database management, among others, providing students with the necessary knowledge and skills to excel in the field. Apart from the quality of education, another advantage of pursuing an analytics course in Thiruvananthapuram is the city's tech-driven ecosystem. The city is home to several leading IT firms such as TCS, Infosys, UST Global, and Tata Elxsi, among others. These companies offer students opportunities for internships, projects, and placements, providing students with practical exposure to real-world applications and industry practices. Moreover, Thiruvananthapuram boasts a thriving startup culture, with several startups focusing on data analytics. Many of these startups collaborate with educational institutions, providing students with an opportunity to work on live projects and gain practical experience. Additionally, students can participate in hackathons, workshops, and seminars hosted by these startups, helping them stay updated with emerging trends and technologies in the field. Apart from the quality of education and the thriving tech ecosystem, another advantage of pursuing a data analytics course in Thiruvananthapuram is the city's quality of life. The city offers students a peaceful and serene environment, away from the hustle and bustle of metropolitan cities. The city's green cover, serene backwaters, and coastal beaches offer students an opportunity to unwind and relax. In conclusion, pursuing a data analytics course in Thiruvananthapuram offers students several advantages in terms of quality education, practical exposure, and an excellent quality of life. With the increasing demand for skilled professionals in the field, pursuing an analytics course in Thiruvananthapuram is a smart choice for students and professionals alike.

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₹70

₹9

Skill Level

Beginner

Internship

Yes

Live Project

2

Certificate

Yes

Live Training

Yes

Career Assistance

Yes

Expiry Period

1 Months
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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 Thiruvananthapuram?

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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