Overview of Analytics Course in Manila

Manila, the capital city of the Philippines, is a bustling metropolis that boasts a thriving business environment. As the country continues to experience rapid growth in various industries, there is an increasing demand for professionals who are knowledgeable in the field of analytics. This has led to the emergence of various analytics courses in Manila, aimed at equipping individuals with the necessary skills to make data-driven decisions. The analytics course offered in Manila is designed to cater to the needs of both beginners and professionals looking to enhance their analytical skills. The course typically covers topics such as data collection, analysis, visualization, and interpretation. The course also focuses on various analytical tools, such as R programming, Python, Tableau, and Excel, which are essential in data analysis. One significant advantage of pursuing an analytics course in Manila is the availability of experienced and highly qualified instructors. These instructors have years of experience in the field of data analysis and are well-equipped to guide students through their learning journey. Another advantage of taking an analytics course in Manila is the opportunity to network with like-minded professionals from various industries. The course attracts individuals from fields such as finance, marketing, healthcare, and information technology, providing students with a chance to learn from their peers and build valuable connections. Moreover, Manila prides itself in having state-of-the-art facilities that are conducive to learning. Most analytics courses are held in modern classrooms equipped with the latest technology, making it easier for students to access learning materials and collaborate with their instructors and classmates. There are also several training centers and institutions in Manila that offer analytics courses in various formats, including online, classroom-based, and hybrid courses. This provides students with the flexibility to choose the format that best suits their schedules and learning preferences. After completing an analytics course in Manila, individuals can expect to secure job opportunities in various industries, such as finance, marketing, and healthcare. They can also pursue careers as data analysts, business analysts, data scientists, and business intelligence analysts, among others. In conclusion, pursuing an analytics course in Manila is a great way to acquire the necessary skills and competencies to work in the field of data analysis. The course provides students with valuable insights into the world of data analysis and equips them with the tools and techniques to make data-driven decisions. With an increasing demand for professionals in data analytics, taking an analytics course in Manila is a smart move that can open up many career opportunities.
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Course Duration

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Internship

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Live Training

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Career Assistance

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Skillfloor Course Training Process
Skillfloor Course Training Process

Internship Certificate

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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 Manila?

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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