Quais são as principais plataformas de análise preditiva para profissionais de análise de dados?

Choosing the right predictive analytics platform is crucial for efficient data analysis. Key features to consider include ease of use, scalability, integration capabilities, and algorithm variety. A user-friendly platform shortens the learning curve, boosting productivity. Scalability ensures smooth performance as data volume increases. Integration with other tools streamlines workflows. A diverse algorithm set enables more sophisticated analysis and better predictive models.

Whenever we are going to choose any analytical platforms we need to understand the requirements and key areas to use that particular platform to perform a data analysis task .The platform that offer robust data-processing and scores high on user-friendliness- this should be our choice .

Microsoft Azure Machine Learning Google Cloud AI Platform Oracle Data Science Platform SAS Advanced Analytics Databricks IBM Watson Studio

Based on my experience, I've found the following predictive analytics platforms to be beneficial for data analysis professionals: Scios, SAS Viya, One Model, Prophet, SAP Analytic Cloud, Qlik, Alteryx AI Platform, Tableau, KNIME Analytics Platform, and IBM Watson Studio. These platforms offer strong predictive capabilities, standalone functionality, integrated machine learning and AI, and diverse data-sourcing capabilities. They are well-suited for various industries and purposes, such as marketing, UX, risk management, and manufacturing. It's important to select a platform that aligns with your specific requirements and data analysis objectives.

IBM SPSS Modeler: User-friendly with comprehensive predictive analytics tools. SAS Predictive Analytics: Enterprise-grade with a wide array of statistical tools. RapidMiner: Drag-and-drop interface with extensive ML algorithm support. Alteryx: Blends data, predictive analytics, and reporting in one platform. KNIME: Open-source, visual tools for data mining and ML. Microsoft Azure ML: Cloud-based suite for ML model development and deployment. Google Cloud AI Platform: Tools for ML model building and deployment on GCP. DataRobot: Automated ML platform for quick model deployment. Databricks: Apache Spark-based analytics platform for scalable data processing and ML. Oracle Data Mining (ODM): Integrated predictive analytics with Oracle Database.

We need to be sure about building proper accurate and reliable models as per the need and also the parameters we are choosing to take an decision and as per the effectiveness of the models we need to take and customize the decision making procedure and parameters in a Particular Model.

Data Collection: Gather relevant data from diverse sources. Data Cleaning: Identify and rectify errors, missing values, and outliers. Data Transformation: Standardize formats, handle categorical variables. Feature Engineering: Create or extract features to enhance analysis. Data Integration: Merge data from different sources while ensuring consistency. Normalization and Standardization: Scale data for uniformity and comparability. Data Reduction: Decrease dimensionality for efficiency without losing key information. Data Splitting: Divide data for training, validation, and testing. Data Formatting: Ensure compatibility with analysis tools. Documentation: Record steps taken for transparency and reproducibility.

Data Collection: Gather relevant data from various sources, including databases, files, APIs, and streaming sources. Ensure data quality by addressing issues such as missing values, duplicates, and inconsistencies. Data Cleaning: Cleanse the data by removing or correcting errors, outliers, and irrelevant information. Impute missing values using techniques such as mean substitution, median imputation, or predictive imputation. Explore the data through descriptive statistics, data visualization, and hypothesis testing to gain insights into underlying patterns, relationships, and anomalies. EDA helps identify potential predictors and understand the distribution and characteristics of the data.

𝗞𝗲𝘆 𝗦𝘁𝗲𝗽𝘀 𝗶𝗻 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗥𝗲𝗮𝗱𝘆 𝗖𝗹𝗲𝗮𝗻 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Find and fix any mistakes or missing information to make sure your data is correct. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Change your data so that everything is in the same format, making it easier to work with. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Make changes or add details to your data to help you analyze it better and make good predictions. 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Put all your data together from different places, ensuring it fits well. 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁: Adjust your data so everything is measured the same way, which helps when you compare different information.

Model Selection: Choose the appropriate type of predictive model based on the nature of the problem, data characteristics, and business objectives. Common types of predictive models include linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and ensemble methods. Hyperparameter Tuning: Fine-tune the model hyperparameters to optimize performance and generalization. This involves selecting the optimal values for parameters such as learning rate, regularization strength, tree depth, and kernel function through techniques like grid search, random search, or Bayesian optimization.

Platforms that offer a wide range of ML algorithms and tools for model selection and hyperparameter tuning can help improve prediction accuracy. These tools enable data scientists to experiment with different algorithms and configurations to find the best model for the data.

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀: Use different methods to analyze your data, like simple comparisons (regression) or more complicated methods like brain-like networks (neural networks). 𝗖𝗵𝗲𝗰𝗸 𝗬𝗼𝘂𝗿 𝗠𝗼𝗱𝗲𝗹: Make sure the platform you use can check if your model is good by using methods like cross-validation, which tests your predictions. 𝗨𝘀𝗲 𝗚𝗼𝗼𝗱 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀: Look at how accurate and reliable your model is. You should check things like accuracy (how often it's right), precision (how exact it is), and recall (how well it finds what it's looking for).

Tableau: Intuitive interface, powerful visualization options. Power BI: Microsoft's tool for interactive reports and dashboards. Plotly: Python library for interactive visualizations. Matplotlib: Python's versatile library for static and dynamic plots. Seaborn: Python library for statistical data visualization. ggplot2: R package for elegant, customizable plots. D3.js: JavaScript library for dynamic web visualizations. QlikView: Business intelligence tool with associative data modeling. Looker: Platform for data exploration and visualization. Google Data Studio: Free tool for interactive dashboards and reports.

Any visualization tools should have the capacity of handling complex data and manage huge data-load when needed and the system should be user-friendly and easily manageable . We can easily communicate complex data Patterns and predictive insights .

One thing I've found helpful is Power BI. I have used both Tableau and Power BI and I found that Power BI is simpler to use. The visualization plots used are self explanatory and even a beginner can learn this too easily. Furthermore, as a student when I wanted to use a tool that can be easily integrated with other tasks, Power BI came to the rescue. Overall, Power BI is a simple tool that anyone can use easily for predictive models.

The top predictive analytics platforms for data analysis professionals often double as robust visualization tools. Tableau, renowned for its intuitive interface and powerful analytical capabilities, stands out for its predictive modeling features. Power BI, another industry leader, seamlessly integrates with Microsoft's ecosystem, offering advanced predictive analytics functionalities alongside its visualization tools. Similarly, QlikView and Qlik Sense excel in data visualization and predictive analytics, empowering users to uncover insights with minimal coding. IBM Watson Studio offers data prep, modeling, visualization. These platform facilitate data exploration and data-driven decision-making through predictive modeling and forecasting.

Power BI: Microsoft Power BI is a business analytics tool that enables users to visualize and share insights from their data. It offers features for data preparation, interactive dashboards, report authoring, and natural language querying. Power BI integrates seamlessly with Microsoft products and other data sources. Choosing the right tool depends on factors such as data complexity, visualization requirements, programming language proficiency, and integration with existing workflows.

𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗛𝗼𝘄 𝘁𝗼 𝗨𝘀𝗲 𝗬𝗼𝘂𝗿 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗙𝗶𝗻𝗱 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: Look for platforms that let you use your model in different ways, like on your own computers (on-premises), in the cloud, or a mix of both (hybrid). 𝗠𝗮𝗸𝗲 𝗜𝘁 𝗪𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗛𝗮𝘃𝗲: The platform should make it easy to add your model to the programs and systems you already use. 𝗨𝘀𝗲 𝗜𝘁 𝗤𝘂𝗶𝗰𝗸𝗹𝘆: You want to be able to start using your insights fast and without a lot of hassle.

For the deployment of data analysis applications, understanding deployment options is key. From cloud-based solutions like AWS SageMaker and Google Cloud AI Platform to on-premises options like IBM SPSS Modeler, diverse deployment choices cater to various organizational needs. Consider factors like scalability, cost-effectiveness, and integration capabilities to select the platform that aligns best with your data analysis goals.

The top predictive analytics platforms for data analysis professionals include IBM SPSS Modeler, SAS Predictive Analytics, RapidMiner, KNIME, Microsoft Azure Machine Learning, and Google Cloud AI Platform. These platforms offer a wide range of advanced analytics capabilities, including machine learning algorithms, data visualization, and model deployment. They vary in terms of features, pricing, and ease of use, so it's important to evaluate them based on your specific needs and requirements.

Selecting the top predictive analytics platform entails assessing key features like user-friendliness, scalability, integration capabilities, and algorithm diversity. Additionally, robust data preparation tools, diverse modeling techniques, advanced visualization features, flexible deployment options, and ongoing support are crucial for effective predictive analysis.

There are so many top predictive analysis platforms for data analytics professionals include IBM Watson Studio, SAS Predictive Analytics, RapidMiner, Different Visualisation tools and Google Cloud AI Platform. They offer advanced machine learning tools, automation, scalability, and integration capabilities, catering to diverse analytics needs.