Data Analytics

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Path 2 (More Focus on Python) 👇👇 Free Resources: https://t.me/pythonanalyst/102 Week 1: Learn Fundamentals Days 1-3: Start with online courses or tutorials on basic data analysis concepts and tools. Focus on Python for data analysis, using libraries like Pandas and Matplotlib. Days 4-7: Dive into SQL basics for data retrieval and manipulation. There are many free online resources and tutorials available. Week 2: Data Analysis Projects Days 8-14: Begin working on simple data analysis projects. Start with small datasets from sources like Kaggle or publicly available datasets. Analyze the data, create visualizations, and document your findings. Make use of Jupyter Notebooks for your projects. Week 3: Intermediate Skills Days 15-21: Explore more advanced topics such as data cleaning, feature engineering, and statistical analysis. Learn about more advanced visualization libraries like Seaborn and Plotly. Days 22-23: Start a personal project that relates to your interests. This could be related to a hobby or a topic you're passionate about. Week 4: Portfolio Completion Days 24-28: Continue working on your personal project, applying what you've learned. Make sure your project has clear objectives, data analysis, visualizations, and conclusions. Day 29: Create a portfolio website using platforms like GitHub Pages, where you can showcase your projects along with explanations and code. Day 30: Write a blog post summarizing your journey and the key lessons you've learned during this intense month. Throughout the month, engage with online communities and forums related to data analysis to seek help when needed and learn from others. Remember, building a portfolio is not just about quantity but also about the quality of your work and your ability to articulate your analysis effectively. While this plan is intensive, it's essential to manage expectations. You may not become an expert data analyst in a month, but you can certainly create a portfolio that demonstrates your enthusiasm, dedication, and foundational skills in data analysis, which can be a valuable starting point for your career. Hope it helps :)

Build Data Analyst Portfolio in 1 month Path 1 (More focus on SQL & then on Python) 👇👇 Week 1: Learn Fundamentals Days 1-3: Start with online courses or tutorials on basic data analysis concepts. Days 4-7: Dive into SQL basics for data retrieval and manipulation. Free Resources: https://t.me/sqlanalyst/74 Week 2: Data Analysis Projects Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings. Week 3: Intermediate Skills Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation. Days 22-23: Explore more advanced SQL topics. Week 4: Portfolio Completion Days 24-28: Continue working on your SQL-based projects, applying what you've learned. Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities. Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code. Hope it helps :)

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Alright! I got a lot of responses from you guys and I will try to reply for most of the concerns in this post New to Data Analytics, want to know how to start? Then here you go 😄👇 Learn SQL & Excel first and then only if you still have some time go for Power BI/ Tableau to improve your visualization skills. If you are also interested in learning a programming language, then go for Python. Freecodecamp & Mode are very good resources to learn these skills. I shared some really good sources to learn them: https://t.me/sqlspecialist/398 Again emphasizing you all to learn SQL if still confused. If you want to practice coding/ SQL questions, then go with Leetcode or Hackerrank You can find more useful resources in these dedicated channels Math/ Statistics is important but even if you aren't good with that, its absolutely fine. If you have time, then go to khanacademy where you'll find pretty useful stuff. Excel 👇👇 https://t.me/excel_analyst Power BI/ Tableau 👇👇 https://t.me/PowerBI_analyst/2 SQL 👇👇 https://t.me/sqlanalyst/29 Python 👇👇 https://t.me/pythonanalyst Statistics Book 👇👇 https://t.me/DataAnalystInterview/34 Free Certificates for data analysis 👇👇 https://t.me/sqlspecialist/433 https://t.me/sqlspecialist/441 Hope I answered most of your questions but if you still need any help, then feel free to connect with me on Linkdin: https://bit.ly/3Zytush Happy learning :)

If you are new to data analytics domain and not sure what to do, then my honest recommendation would be to start learning SQL & Excel. If not sure from where to learn then I already shared a lot of resources in this channel, just pick up one and stick to it. Don't start something new until you finish it. Feel free to reach out to me @coderfun if you need any help. Will be more than happy to help you😄❤️

Free Certificates to become a data analyst from Freecodecamp, Hackerrank, Udacity, Kaggle & many more 👇👇 https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k?utm_source=share&utm_medium=member_android

Pandas in 5 minutes 👇👇 https://www.linkedin.com/posts/sql-analysts_learn-pandas-in-5-minutes-activity-7112717504854003712-4fzj?utm_source=share&utm_medium=member_android Save the post and like it if need for like these 😄👍

5⃣ Project ideas for a data analyst in the investment banking domain M&A Deal Analysis: Analyze historical mergers and acquisitions (M&A) data to identify trends, such as deal size, industries involved, or geographical regions. Create visualizations and reports to assist in making informed investment decisions. Risk Assessment Model: Develop a risk assessment model using financial indicators and market data. Predict potential financial risks for investment opportunities, such as stocks, bonds, or startups, and provide recommendations based on risk levels. Portfolio Performance Analysis: Evaluate the performance of investment portfolios over time. Calculate key performance indicators (KPIs) like Sharpe ratio, alpha, and beta to assess how well portfolios are performing relative to the market. Sentiment Analysis for Trading: Use natural language processing (NLP) techniques to analyze news articles, social media posts, and financial reports to gauge market sentiment. Develop trading strategies based on sentiment analysis results. IPO Analysis: Analyze data related to initial public offerings (IPOs), including company financials, industry comparisons, and market conditions. Create a scoring system or model to assess the potential success of IPO investments. ENJOY LEARNING 👍👍

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Glad to see the amazing response from you guys 😄 Here are the answers to these questions Explain the Data Analysis Process: The data analysis process typically involves several key steps. These steps include: Data Collection: Gathering the relevant data from various sources. Data Cleaning: Removing inconsistencies, handling missing values, and ensuring data quality. Data Exploration: Using descriptive statistics, visualizations, and initial insights to understand the data. Data Transformation: Preprocessing, feature engineering, and data formatting. Data Modeling: Applying statistical or machine learning models to extract patterns or make predictions. Evaluation: Assessing the model's performance and validity. Interpretation: Drawing meaningful conclusions from the analysis. Communication: Presenting findings to stakeholders effectively. What is the Difference Between Descriptive and Inferential Statistics?: Descriptive statistics summarize and describe data, providing insights into its main characteristics. Examples include measures like mean, median, and standard deviation. Inferential statistics, on the other hand, involve making predictions or drawing conclusions about a population based on a sample of data. Hypothesis testing and confidence intervals are common inferential statistical techniques. How Do You Handle Missing Data in a Dataset?: Handling missing data is crucial for accurate analysis: I start by identifying the extent of missing data. For numerical data, I might impute missing values with the mean, median, or a predictive model. For categorical data, I often use mode imputation. If appropriate, I consider removing rows with too much missing data. I also explore if the missingness pattern itself holds valuable information. What is Exploratory Data Analysis (EDA)?: EDA is the process of visually and statistically exploring a dataset to understand its characteristics: I begin with summary statistics, histograms, and box plots to identify data trends. I create scatterplots and correlation matrices to understand relationships. Outlier detection and data distribution analysis are also part of EDA. The goal is to gain insights, identify patterns, and inform subsequent analysis steps. Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem: In a previous role, I worked for an e-commerce company, and we wanted to reduce shopping cart abandonment rates. I conducted a data analysis project: Collected user data, including browsing behavior, demographics, and purchase history. Cleaned and preprocessed the data. Explored the data through visualizations and statistical tests. Built a predictive model to identify factors contributing to cart abandonment. Found that longer page load times were a significant factor. Proposed optimizations to reduce load times, resulting in a 15% decrease in cart abandonment rates over a quarter. Hope it helps :)

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5⃣ Important data analysis interview questions Explain the Data Analysis Process: Walk me through the typical steps you follow when conducting a data analysis project. What is the Difference Between Descriptive and Inferential Statistics?: Can you explain the distinction between descriptive statistics and inferential statistics and provide examples of when each is used? How Do You Handle Missing Data in a Dataset?: What strategies and techniques do you use to deal with missing or incomplete data in a dataset? What is Exploratory Data Analysis (EDA)?: Describe what EDA is and the various methods and visualizations you employ during this phase of data analysis. Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem: Share a specific project or scenario where you applied data analysis techniques to address a practical problem. What was the outcome, and what tools or methodologies did you use? Like this post if you also need the answers for the above questions ❤️👍

Important Python concepts to become a data analyst 👇👇 https://www.linkedin.com/posts/sql-analysts_python-for-data-analysis-activity-7111251746722623488-bff0?utm_source=share&utm_medium=member_android Join our Linkedln page to learn data analysis and get job opportunities 👇👇 https://www.linkedin.com/company/sql-analysts/

Nice to see the amazing response from you guys 1600+ voted for free certifications. So here we go : Alteryx: https://community.alteryx.com/t5/Certification-Exams/bd-p/product-certification Python: https://www.freecodecamp.org/learn/data-analysis-with-python/ https://www.hackerrank.com/skills-verification/python_basic Data Visualization: https://www.freecodecamp.org/learn/data-visualization/#data-visualization-with-d3 SQL: https://www.hackerrank.com/skills-verification/sql_basic https://www.hackerrank.com/skills-verification/sql_intermediate https://hackerrank.com/skills-verification/sql_advanced Hope it helps :)

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Here's a list of 50+ Python libraries for data science👇 https://t.me/pythondevelopersindia/678