What is Data?

Technically, data is raw facts, figures, or values in text, numbers, or symbols. It is computers' basic unit of information and may be processed, examined, and modified to gain insights.

Data underpins all computing operations, analyses, and logical processes. This raw material powers computations, allowing meaningful insights and the execution of operations and algorithms.

Where can we obtain data for analysis?

• Internal Databases:

  • Extract relevant information from your organization's databases, including customer records, sales data, and operational metrics.

• Data Warehouses:

  • Utilize centralized repositories that consolidate data from various sources within your organization for comprehensive analysis.

• Public Datasets:

  • Explore platforms like Kaggle, UCI Machine Learning Repository, and data.gov for freely available datasets spanning diverse domains.

• APIs (Application Programming Interfaces):

  • Access data programmatically from online services via APIs, such as financial market data, social media metrics, or weather information.

• Web Scraping:

  • Extract data from websites through web scraping techniques, ensuring adherence to ethical considerations and legal regulations.

• Government and NGO Data Portals:

  • Access datasets from government agencies and non-governmental organizations covering demographics, public health, economic indicators, and more.

Always focus on the future goals and don't ignore them, but learn from Learn from the past experiences like a ML model.

What is Analysis & Analytics?

Analysis:

Scope: - Analysis involves analyzing facts, information, or situations to comprehend its components and make sense of them.
Nature: - Qualitative or quantitative analysis may simplify difficult information for greater comprehension.
Example: - Financial analysts evaluate company performance and recommend investments using financial statements.

Analytics:

Scope: - Analytics refers to the application of technology, methodologies, and tools to analyze and interpret data, aiming to provide insights and aid decision-making.
Nature: -Analytics uses advanced statistical and computational tools like predictive modeling and machine learning to find data patterns and trends.
Example: Business analytics can identify trends, streamline processes, and boost efficiency using data from several sources.

Data Analytics

Data analysis is a broad process that includes exploring datasets in great detail, cleaning them up carefully, manipulating them in transformative ways, and building models from them. Its goal is to uncover insights, form conclusions, and aid decision-making. Data Analysts examine data complexity in a business. This deep dive into data complexity involves careful attention to patterns, trends, and deeper insights for educated business decisions. In addition, a good Data Analyst works well with Data Engineers to maximize an organization's data potential.

Types of Data Analytics:

Descriptive Analysis:

• Objective: Summarizes and describes the main features of a dataset.

• Method: Involves measures of central tendency, variability, and visualization techniques.

Diagnostic Analysis:

• Objective: Identifies patterns or trends to understand why a particular event occurred.

• Method: Investigates cause-and-effect relationships, often using statistical tests.

Predictive Analysis:

• Objective: Forecasts future trends or outcomes based on historical data.

• Method: Involves machine learning algorithms and statistical modeling.

Prescriptive Analysis:

• Objective: Recommends actions to optimize future outcomes based on analysis.

• Method: Combines insights from descriptive, diagnostic, and predictive analyses to prescribe strategies.

How to Become a Data Analyst?

1. Educational Background: - Develop solid math, stats, and CS skills. Statistics, mathematics, computer science, and data science degrees are widespread.

2. Learn Data Analysis Tools: - Master Python or R for data analysis. Learn Python's Pandas, NumPy, and scikit-learn or R's tidy verse.

3. Database Knowledge: - Recognize databases and SQL for data retrieval and processing. Learn about relational and non-relational databases.

4. Develop data visualization skills using software such as Matplotlib, Seaborn, or ggplot2. Visualizing information for non-technical stakeholders is essential.

5. Excel Proficiency: - Excel is commonly used for data analysis. Learn sophisticated Excel data cleaning, transformation, and analysis features.

6. Statistical Knowledge: - Improve statistical comprehension. Learn to use statistics to draw inferences from data.

7. Machine Learning Basics: - Learn about machine learning ideas. Know supervised and unsupervised learning and when to use algorithms.

8. Critical Thinking: - Develop analytical problem-solving skills. Learn how to ask good questions and gain insights.

9. Communication Skills: - Effective communication is essential. Prepare to communicate complex analyses and findings to technical and non-technical audiences.

10. Create a Portfolio: - Display your expertise through a project portfolio. Showcase your data analysis, visualization, and machine learning efforts.

11. Networking and Continuous Learning: — Network with other professionals in the sector. Keep up with industry changes and keep learning.

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This begins a weekly Data Analysis series. Data Analysis blog posts are published every Friday. Get ready for some fresh perspectives, helpful hints, and a better grasp of how data analytics is changing!