You're about to start a data analytics project. What's the first thing you need to consider?

- Articulate project goal and objective - Understand stakeholder expectations and define timelines accordingly - Gather data from various sources as needed - Define KPI's and work on creating a prototype - Data preparation, cleaning, EDA, modelling if required - Dashboard creation / Insights generation - Communicate results

Antes de qualquer projeto de análise de dados, é muito importante saber como estão as fontes de dados que serão trabalhadas, no quesito, estrutura de dados e principalmente, qualidade dos dados. Esses dois pontos são cruciais para uma estimativa de projeto e análise de dados de sucesso.

When embarking on a data analytics project, the first and foremost consideration is to clearly define your objectives. Understanding the problem you aim to solve or the questions you want to answer will guide every subsequent step. This involves: 1. Identifying Key Goals: Determine the specific insights or outcomes you need. 2. Stakeholder Alignment: Ensure all stakeholders are on the same page regarding objectives. 3. Data Requirements: Ascertain what data is necessary to achieve these goals. 4. Success Metrics: Define how success will be measured to evaluate the project's impact.

Initiate a successful data analytics project by prioritizing goal definition. Clearly articulate the question to answer, problem to solve, or desired outcome. Ensure goals are specific, measurable, guiding your data collection, analysis, and communication. A well-defined goal prevents resource wastage on irrelevant or misleading data, ensuring project efficiency and relevance.

Define the project's purpose and scope, outlining the specific questions or problems you aim to address. Understand the stakeholders' expectations and requirements to align the project with their needs. Establish measurable goals and key performance indicators (KPIs) to track progress and success. Consider the available data sources and their relevance to your objectives. Ensure clarity on the timeframe and resources available for the project. Finally, create a project plan outlining tasks, milestones, and responsibilities to guide the execution of the data analytics project.

Let's say you're a data analyst for a retail company. You're tasked with understanding customer behavior and identifying trends in customer purchases. One internal data source you could use is the company's sales data. This data would include information on each purchase, such as the customer's name, the date of purchase, the items purchased, and the price paid. Another internal data source you could use is the company's customer loyalty program data. This data would include information on each customer, such as their name, contact information, and purchase history. By combining data from these different sources, you could gain a more comprehensive understanding of customer behavior and identify trends in customer purchases.

Selecting appropriate data sources is crucial for a successful data analytics project. After defining your goal, evaluate internal and external sources based on quality, availability, accessibility, and suitability. Additionally, consider ethical and legal implications associated with the data to ensure responsible and compliant usage. Careful consideration at this stage lays a strong foundation for effective data-driven insights.

For the second step, I should select my data sources. These can be internal or external, structured or unstructured, qualitative or quantitative. It's crucial to assess their quality, availability, accessibility, and suitability for my goal. Additionally, I must consider the ethical and legal aspects of data usage.

Choosing data sources is like picking ingredients for a recipe. 🍳 Once you know what you want to cook up (your goal), you select the best ingredients (data sources) to make it happen. These sources can be inside or outside your organization, and they come in different types and forms. When choosing, you look at factors like quality, availability, and how well they fit your needs. But just like checking expiration dates on groceries, you also consider ethical and legal concerns when using certain data sources. It's all about selecting the right mix for a successful outcome! 📊👩⚖️

La recolección de datos, más que una simple tarea, es la piedra angular del análisis. Es la etapa donde se define la calidad y el potencial de las futuras conclusiones. Si bien la selección de las fuentes es crucial, la planificación de la recolección es donde la visión del analista cobra vida. No se trata de obtener "toda la información posible", sino de identificar los datos específicos que respondan a las preguntas de negocio. La frecuencia de la recolección dependerá de la dinámica del problema a analizar. La confianza en los resultados depende de la calidad de los datos.

Efficient data collection planning is vital post data source selection. Define what, how much, and how often to collect data. Ensure reliable, valid, and consistent methods to organize and store data. A well-thought-out data collection strategy lays the groundwork for accurate and meaningful analyses in your data analytics project.

After selecting my data sources, I must plan my data collection process. This involves deciding what data to collect, how much, and how often. Additionally, I need to determine storage and management methods, ensuring reliability, validity, and consistency in data collection .

Planning your data collection is like mapping out a scavenger hunt! 🗺️ Once you've identified where to look for treasures (your data sources), you need a plan to gather them effectively. Decide what specific data you need, how much you need, and how often you'll collect it. Just like marking spots on your map, you'll want to ensure your collection methods are reliable and consistent, so you don't miss any valuable clues. And just like keeping your treasures safe, you'll need to think about how to store and manage your data securely. With a well-planned approach, you'll be sure to gather the data you need for your journey! 📊🔍

1.Start by clearly defining the objectives and key questions you want to answer with your analysis. This will guide the selection of appropriate analytical techniques and metrics. 2. Thoroughly explore and understand your dataset, including its structure, variables, and any potential issues or limitations. This will inform the preprocessing steps and analytical approaches. 3.Based on your goals and data characteristics, choose the suitable analytical techniques, such as descriptive statistics, inferential statistics, machine learning algorithms, or data mining methods. 4.Identify the relevant variables and features from your dataset that are crucial for answering your questions and achieving your objectives.

Determining the question or goal you're attempting to answer in a data analytics project should come first, even though designing the analysis is important. The direction of the entire process is established by this first step. It assists you in selecting the kinds of data you must gather, the analysis techniques you'll employ, and the appropriate hardware and software. In the absence of a well-defined inquiry or goal, you run the risk of gathering superfluous information, selecting unsuitable techniques for analysis, and ultimately coming to unreliable or meaningless conclusions.

Thoughtful design is essential for effective data analysis. Decide on analysis type, tools, variables, and indicators. Acknowledge assumptions and limitations. Ensure appropriateness, accuracy, and transparency in your chosen data analysis methods. A well-designed analysis enhances the reliability and interpretability of insights derived from your data analytics project.

Designing data Analysis is very important part when we are doing data Analytics Project.Data Analysis means to understand your data and for this understanding we have to discover different patterns,trends and find relationship between different features.so for data analysis there are different tools you have to use like google colab,jupyter notebook in that use python pandas and numpy libraries and microsoft excel for data cleaning puprposes etc...

After preparing my data, I need to design my data analysis process. This involves selecting the appropriate statistical techniques and tools to discover patterns, trends, and relationships. I must decide on variables, indicators, assumptions, and acknowledge limitations. It's essential to ensure the accuracy, appropriateness, and transparency of my data analysis methods.

Listen to the stakeholder, ask again and then most importantly confirm your understanding to ensure that you have heard, captured the requirements and are aligned with the stakeholder with the goal of the project. Alignment not only brings in understanding of the task but also the priority / urgency from a business perspective which is all important to set right at the beginning.

With every data analytics project, make sure that we start with what is the goal of the project. Which problem are we trying to solve? It is very critical to hear it directly from the project sponsor - since everyone will pick up different things from the talk. Once the problem to be solved - common goal is established - rest will fall in place.

Preparing your data communication is like crafting a compelling story! 📊✨ Once you've uncovered insights from your analysis, you need to share them effectively. Decide on the best format and medium to reach your audience, whether it's a presentation, report, or interactive dashboard. Craft a clear and engaging message, weaving together the key findings into a cohesive narrative. Just like illustrating your story with vivid pictures, choose visualizations and charts that enhance understanding. And, like a skilled storyteller, make actionable recommendations that drive your audience to take the desired steps. With thoughtful preparation, your data communication will captivate and persuade your audience! 📈🗣️

, I need to prepare data communication. This involves selecting formats, mediums, and visualizations to convey analysis results effectively. Crafting a clear message, providing recommendations, and ensuring engagement and persuasion are crucial. It's vital to maintain clarity and coherence in data communication methods.

Analyze the available data and provide the insights how data can be driven for business decisions. Share with the stakeholders with the insights could be excel pivot charts, power points , mockup the dashboards/reports with any wireframe tools. Taje the feedback from the stakeholders and incorporate those changes and plan for development and integrations. Until the development is complete plan for delivering the data insights manually.

t's crucial to review my data project. This involves evaluating successes and failures, learning lessons, and identifying insights. Using metrics and criteria, I'll determine changes and improvements needed, ensuring a constructive, honest, and collaborative approach to project review.

Before embarking on a data analytics project, the foremost consideration is to thoroughly review the project plan. This entails assessing the project's objectives, scope, and feasibility in alignment with organizational goals. It's essential to evaluate available resources, including data sources, tools, and expertise, to ensure they are adequate for project requirements. Additionally, clarifying stakeholder expectations and obtaining buy-in are crucial initial steps. By conducting a comprehensive review upfront, potential challenges can be identified early, allowing for effective mitigation strategies to be devised.

Before starting a data analytics project; it's best to start first by defining and understanding the problem I need to solve so that I'd be able to define my objectives and methodologies to solve this problem

Before starting a data analytics project, the first thing to consider is defining clear objectives and goals. Understand what you aim to achieve with the project, whether it's improving a process, solving a specific problem, or gaining insights into a particular aspect of the business. This clarity will guide your methodology, data collection, analysis, and interpretation, ensuring that every step of the project aligns with the ultimate goal. Establishing these objectives early on facilitates focused research, efficient use of resources, and actionable outcomes.

Share the next set of insights which could be delivered which the data team has analyzed. Get the feedback from the stakeholders.