Data Analysis Books

Want to become a data analyst? Stage 1 – Excel Stage 2 – SQL + Project Stage 3 – Python (Pandas, NumPy) + Project Stage 4 – Data Visualization (Matplotlib, Seaborn) + Project Stage 5 – Statistics + Project Stage 6 – Machine Learning (Scikit-learn) + Project Stage 7 – Big Data Tools (Hadoop, Spark) + Project 🏆 – DataAnalytics

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Data analytics essential concepts 1. Descriptive Analytics: Descriptive analytics involves analyzing historical data to understand what has happened in the past. It focuses on summarizing and interpreting data to describe patterns, trends, and relationships. 2. Predictive Analytics: Predictive analytics uses statistical algorithms and machine learning techniques to predict future outcomes based on historical data. It helps organizations make informed decisions by forecasting trends and identifying potential risks or opportunities. 3. Prescriptive Analytics: Prescriptive analytics goes beyond predicting outcomes and provides recommendations on what actions to take to achieve a desired outcome. It leverages optimization and simulation techniques to suggest the best course of action. 4. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It helps in understanding complex data sets, identifying trends, and making data-driven decisions. 5. Data Mining: Data mining is the process of discovering patterns, trends, and insights from large datasets using statistical and machine learning techniques. It involves extracting valuable information from data to support decision-making. 6. Machine Learning: Machine learning is a subset of artificial intelligence that enables systems to learn from data and make predictions or decisions without being explicitly programmed. It involves algorithms that improve their performance over time through experience. 7. Natural Language Processing (NLP): Natural Language Processing is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It is used in applications such as sentiment analysis, chatbots, and language translation. 8. Sentiment Analysis: Sentiment analysis is the process of analyzing text data to determine the sentiment or opinion expressed in it. It is commonly used in social media monitoring, customer feedback analysis, and market research. 9. Data Warehousing: Data warehousing involves collecting, storing, and managing large volumes of structured and unstructured data from various sources. It provides a centralized repository for data analysis and reporting. 10. Data Governance: Data governance refers to the overall management of data assets within an organization. It includes policies, processes, and controls to ensure data quality, security, privacy, and compliance with regulations. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

Top 5 skills for DataAnalytics 1. Proficiency in programming languages like Python, R, or SQL. 2. Strong analytical and problem-solving skills. 3. Ability to work with data manipulation and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn. 4. Knowledge of statistical analysis and machine learning techniques. 5. Effective communication and storytelling skills to convey insights from data to stakeholders.

You don't need to know everything about every data tool. Focus on what will help land you your job. For Excel: - IFS (all variations) - XLOOKUP - IMPORTRANGE (in GSheets) - Pivot Tables - Dynamic functions like TODAY() For SQL: - Sum - Group By - Window Functions - CTEs - Joins For Tableau: - Calculated Columns - Sets - Groups - Formatting For Power BI: - Power Query for data transformation - DAX (Data Analysis Expressions) for creating custom calculations - Relationships between tables - Creating interactive and dynamic dashboards - Utilizing slicers and filters effectively

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If you're looking to build a career in Data Analytics but feel unsure about where to start, this post is for you. It's important to know that you don't need to spend money on expensive courses to succeed in this field. Many posts you see on LinkedIn promoting paid courses are often shared by individuals who are either trying to sell their own products or are being compensated to endorse these courses. Through this post, I will share with you everything you need to start your data journey absolutely free. Source

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