You’re new to data science. Which tools will help you get started?

Programming Languages: Python: Widely used for data analysis and machine learning. Learn libraries like NumPy, Pandas, and Matplotlib. R: Another popular language for statistical computing. Ideal for data exploration and visualization. Integrated Development Environments (IDEs): Jupyter Notebooks: Interactive and great for experimenting with code, visualizing data, and documenting your analysis. RStudio: An IDE for R that provides a user-friendly environment for data analysis and visualization. Version Control: Git: Essential for tracking changes in your code. Learn the basics of version control to collaborate effectively. Data Manipulation and Analysis: Pandas: A Python library for data manipulation and analysis.

Following skillsets are extremely important for a newbie in Data Science: - Good exposure to a programming language (preferably Python) - Database querying language - SQL - Statistics and Machine Learning fundamentals - Data Visualization Tool (ex - Tableau, PowerBi etc) - Problem solving skills

As a budding Data Scientist, your first step is to dive into a programming language, and right now, the heavyweights in the field are Python and R. I'd personally lean towards Python—it's versatile and widely adopted. To kickstart your coding journey, Coursera is an awesome learning platform. Once you've got the programming basics down, it's time you focus on enhancing your visualization's skills. Power BI and Tableau are go-to Dashboarding tools for bringing your data to life. Last but not least, storytelling is a superpower in Data science. To weave your insights into compelling narratives, tools like PowerPoint or Canva can be your best buddies.

The main programming to learn will be Python, it is the most used, it has the biggest community and it allows a lot more use cases outside of data science that R cannot compete against. The other language to learn will be SQL in order to query large amount of data.

For me, a very direct advice would be: learn Python. Yes, I know the programming language is not the most important aspect of our field, and that there are R and other languages used in data science, but I think we don't need to complicate things for beginners. They already have enough to worry about. Besides the advantage of Python being the most popular language in the industry, I believe that having this contact with data increases student engagement with their studies, making them feel like they're already part of the data community. Of course, I'm assuming they don't know any language. Otherwise, if they are, for example, R users, they should continue with R! The goal is not to overwhelm the student in the beginning!

If you cannot find exact raw data needed for your project or specific subject, open APIs provided by various websites can be a valuable resource for collecting data. For eg, Reddit, provides open APIs that allows us to collect comments, discussions and posts and other data related to specific topics or subjects.

?? Working on real-life datasets is crucial for aspiring data scientists. It allows you to apply your skills to practical problems, exposing you to the complexities of real-world data. While library datasets are great for learning specific techniques, real-life projects provide a deeper understanding of data challenges and showcase your ability to handle business or research-related data. ???? Dive into a real-life project to accelerate your growth! ?????

One of the best ways to start your journey on data science is trying on projects from scratch, but to do that you need trusted databases. There are many websites where you can find some but I would recommend kaggle. Soon as you get more confortable with it you can try competitions.

There are a lot of ways to get data for starting any project like: - Web scrapping - APIs - Online plateform such as Kaggle.( I always use kaggle) - Collected by yourself Then to access data you need tool like BeautifulSoup, Selenium, SQL, pandas, Excel etc.

a few different places offer free data sources to help you get started: - Government bodies (ONS, Land Registry in the UK) have open data sets, that can be complex and massive in size - Kaggle - has both data sets and competitions/training use cases where you can download both the data, and see other peoples answers/work for inspiration - Google has a hidden datasets search feature, where you can also find tagged paid/free datasets to use

Data cleaning is a crucial step in any data science project. It involves identifying and correcting errors, inconsistencies, and missing values in your data. In Python libraries like pandas offer powerful data manipulation capabilities, while sklearn.preprocessing provides tools for scaling and encoding data. In R packages like dplyr and tidyr excel in data cleaning tasks, helping you transform your data into tidy, analysis-ready formats. Data cleaning is an iterative process. As you explore your data further, you might need to revisit cleaning steps and address new issues that emerge.

Mastering data cleaning is important to newcomers in data science. Tools such as the Pandas library in Python are vital for dealing with operations such as filling gaps, eliminating duplicates, and correcting inconsistencies. Assume you need to analyze a large customer feedback dataset; Pandas can help clean and organize the data, enabling more effective analysis. Furthermore, R’s dplyr package is efficient in data manipulation and cleaning especially when very large datasets are involved. OpenRefine is another client-friendly tool that beginners can use to clean messy data. Obtaining these skills means that the very basis of your data science project—the data itself—is accurate, trustworthy, and ready for analysis.

Invest in mastering data cleaning skills using Python's Pandas library, R's dplyr package, and OpenRefine to ensure your data science projects' foundations are built on accurate, reliable raw data. Cleaning enhances success, credibility, and sets up data-driven discoveries by readying data for analysis.

???? Data cleaning is often underestimated, especially when working with pristine training datasets. However, in real-life projects, data can be messy and require meticulous cleaning. Investing time in data cleaning and preparation is crucial for accurate analyses and meaningful insights. Remember, the cleaner the data, the smoother the journey toward valuable results! ????

One of the important steps in Data Science is Data cleaning. If your data is not cleaned properly then no matter what model you are trying on your data, you can not get good results. After reading the dataset, it is important to ensure the quality of the data. You need to perform various steps like checking missing values, outliers, duplicate values, not relevant data. You can do this with the help of numpy, pandas, and matplotlib packages in Python.

Exploring the data is like being a detective on a fascinating journey! ???♂??? It's the initial step where you unearth insights and uncover the hidden gems within the dataset. Channeling your inner investigator is key! ???♀??? To excel in this step, delve into best practices, explore other projects, and consult your trusty statistics books and notebooks. Remember, statistics is your compass in this thrilling exploration! ????

- Mastering data analysis methods is key. These methods include descriptive and inferential statistics, hypothesis testing, and various machine learning techniques. - Data analysis allows you to extract meaningful insights from your data, whether it's identifying patterns, testing assumptions, or making predictions. - An example of the tools needed for effective data analysis includes numpy for numerical operations, scipy and statsmodels for more advanced statistical tasks, sklearn for a wide range of machine learning algorithms, and tensorflow for deep learning applications. Each of these tools plays a crucial role in exploring, understanding, and interpreting data in data science projects.

For more advanced analysis, tools like Jupyter Notebooks are essential. These interactive environments allow you to combine code, visualizations, and explanations, providing a collaborative and transparent approach to data analysis.

Antes del Análisis de datos es importante entender conceptos de modelado de datos, como tipos de modelo, conocer principales como ER y modelo relacional, Pk y Fk, tipos de relaciones entre tablas, cardinalidad de las relaciones. Un correcto modelado no sólo es disparador esencial para un análisis más sencillo sino también una herramienta rápida de detección de errores.

To data science, several powerful tools are instrumental in conducting data analysis, answering questions, testing assumptions, finding patterns, and making predictions. 1. ???????????? (????????????, ??????????, ????????????????????, ??????????????): General-purpose programming language with robust libraries. 2. ?????????????? ??????????????????: Open-source web application for creating and sharing documents. 3. ?????????????? / ?????????? ???? / ???????????? / ???????????????? / ????????????:Data visualization and BI tool. 4. ??????: Query language for managing and manipulating relational databases. 7. ???????????? ??????????: Cloud-based Jupyter notebooks by Google. The choice of tools depends organization's preferences.

Data visualizations condense complex data into easy formats,?like charts,?graphs,?maps,?dashboards,?and even interactive applications. In Python libraries like Matplotlib offer basic plotting capabilities, while Seaborn excels in creating more informative visualizations. For interactive elements, Plotly is more preferred. In R the ggplot2 package reigns supreme, providing a framework for crafting visually impactful stories with your data. For interactive storytelling, Shiny allows you to build powerful web. Prioritize clear communication over overwhelming visuals. Choose the right chart type, avoid information overload, and use color effectively.

Time to step into the spotlight! ???? Previously, your work was like a hidden treasure, but now it's showtime! From this step onward, your findings need to be showcased through compelling visualizations. ??? Crafting engaging visual narratives is the name of the game, and storytelling is your superpower! ???? Shine a light on the insights and take your stakeholders on an exciting data-driven journey! ????

There are some greate visualizaiton options which i will mention only 3: 1-Matplotlib : generates static images of plots. 2-Plotly: Interactive plotting(slower) 3-Seaborn: Statistical plotting (built on top of matplotlib) with more features and a better look and feel.

- Data visualization is crucial for effectively communicating your findings. It involves using charts, graphs, maps, and interactive dashboards to present data in an understandable and engaging manner. - Good visualization highlights key insights and messages in your data, making complex information more accessible. - An example of visualization tools includes matplotlib and seaborn for Python, which are great for creating a wide range of static charts and graphs. Plotly offers interactive plotting capabilities. For R users, ggplot2 is a powerful tool for creating sophisticated visualizations, and shiny is used for building interactive web applications. These tools help in translating complex data insights into clear visual stories.

Data visualization is an essential tool for presenting data insights. It involves creating charts, graphs, maps, and interactive dashboards. Effective visualization communicates findings clearly and engagingly, emphasizing key messages. Tools like matplotlib, seaborn, plotly, ggplot2, and shiny are widely used for these purposes. For clear and compelling data presentation, visualization techniques are crucial. They transform complex data into understandable visuals. Use matplotlib and seaborn for basic charts and graphs, plotly for interactive plots, and ggplot2 and shiny for advanced and dynamic visualizations.

Data is indeed the new oil, and with great power comes great responsibility! ????? As a data scientist, navigating the legal landscape is crucial. ???? One wrong step, and you might find yourself in a legal gorge! ???? Ethics should be your compass; avoid p-hacking pitfalls and stay true to proper practices. ???? Your data integrity is your armor against the legal dragons. ????? Stay vigilant and uphold the ethical code of the data realm! ????

Data ethics is a crucial aspect of data science. For instance, when working with user data, you need to ensure privacy and security. Consent from users for data collection and fair use of data are also important. Tools like data protection laws guide you in handling data ethically. Ethical codes provide a framework for accountability and transparency. Remember, applying data ethics not only protects the users' data but also builds trust in your data science projects.

Data ethics, often overlooked, play a crucial role in promoting fair and transparent practices. Predictive models commonly mirror human biases, for instance, an algorithm for approving loans may discriminate based on zip codes, indirectly reflecting racial or socioeconomic disparities. Respect privacy and always get consent for data usage, while also being transparent about how data is handled. Your choices have the power to shape a more just and ethical future in data usage.

Understanding Bias and Fairness: Resources: Readings on ethical considerations in data science, such as "Weapons of Math Destruction" by Cathy O'Neil. Legal and Privacy Issues: Resources: Familiarize yourself with data protection regulations (e.g., GDPR, HIPAA).

As we work with data, it is imperative to consider ethical issues. Tools like IBM's AI Fairness 360 can help detect and mitigate bias in machine learning models, promoting ethical practices in data science.

I failed my first data science interview because I didn't know BERT. However, after the interview, I quickly learned BERT by completing two guided projects on Coursera and emailed the hiring manager. He was impressed by my ability to learn new skills quickly, that he advance me to the next round of the interview process. And, guess what? I got the job! In my opinion, project-based learning is the best way to learn new data science skills. Coursera is my favorite resource because they offer guided projects to help learn new skills. Project-based learning is more effective than learning the traditional way because it allowd you to jump right in apply your skills.

Specialize - pick a few focus areas that you like and see a market niche, from e-commerce to predictive analytics or LLMs! Data science is so vague these days.

Ao longo da jornada de estudos para se tornar cientista de dados é comum sermos fisgados por forma??es altamente completas e passarmos horas e horas estudando diversas ferramentas. Mas na verdade deveríamos focar no básico inicialmente. Sabe o princípio de Pareto? é isso! Aprenda bem os 20% que resolvem 80% dos problemas das empresas, pois assim você conseguirá o quanto antes uma oportunidade para trabalhar na área e come?ar a pegar experiência e ganhar dinheiro para arcar com outros cursos ou até certifica??es.

To get started in data science, some essential tools and libraries include Python, Jupyter Notebook for interactive development, NumPy for efficient numerical computing, Pandas for data manipulation and analysis, Matplotlib for data visualization, Scikit-learn for machine learning algorithms, TensorFlow for deep learning, Keras for building neural networks, PyTorch for flexible deep learning models, and SQL for managing and querying databases. These tools provide a solid foundation for data exploration, analysis, modeling, and visualization, allowing you to tackle a wide range of data science tasks.

With the ever-evolving data landscape, new tools and technologies emerge constantly. Data Science is much more than tools. It is a Data-driven problem-solving methodology. Prioritize more on the Statistical Concepts, Database management, Exploratory Data Analysis concepts, Feature Engineering, and fitting visualizations to represent the data. Practising these foundational concepts and building upon these further will help us transition into data science.