Here's how you can master the most in-demand programming languages for data scientists in new technology.

Mastering Python basics is essential for any data scientist. Think of it like learning a new recipe—start with the core ingredients. Each day, spend time writing simple Python scripts to automate mundane tasks, like organizing your files or scraping web data for a project. Gradually, as you become more comfortable, tackle more complex problems using Python libraries like Pandas and NumPy. This daily practice not only builds your confidence but also equips you with a versatile toolset for data manipulation and analysis. 🍰🔧 #DataScience #PythonProgramming #LearnDaily

I have trained around 3000 students, and in my experience before before you start programming for data science, explore the problems data science can solve and then start learning the language. Here is how you can do it: 1. Learn different problems Data Science/AI can solve from Kaggle, from learning section do the tutorials in Python 2. Now pick 1 or 2 simple problems from Kaggle and start working on them 3. Once you get your hands dirty, then start with exploring data with Pandas 4. Now get into Numpy & Stats with python 5. Later Machine Learning, Forecasting with python 6. Best place to learn the basics is either by browsing courses from freecodecamp or Oreilly books related to Data science with python I prefer books!

Learn python and apply it parallely and don' learn only regarding data handling, data manipulation part try to explore as much into python os that you will get familiar with all types of problems handled by python and coming to dat science and data engineering we need data we get data from data base or csv file or excel file but sometimes you need to do data augmentation by extracting new data from web resource or data present in pdf or document files etc for this we will be using Web Scraping and Ocr and to make our code simpler,readable and authenticated and we use api as it used as mediator between four source and client. Python plays a major in data engineering as well building data pipelines and coming to data science building LLM's.

It goes something like this: Master python from the perspective of problem solving, personally I did this through questions on hackerrank in DSA Delve into Python for Data Science learning the basics and where to apply tools like NumPy, Pandas, MatPlotLib, Scikit-learn,... Mastering these is an iterative process of taking data from online sources like kaggle and going through the DS lifecycle on them After becoming accustomed to the tools, you go into more in depth topics such as Deep Learning frameworks, LLMs, deployment

To master the most sought-after programming languages for data scientists in today's rapidly evolving technology landscape, it's crucial to start with a solid foundation in key languages like Python, R, and SQL. Python's versatility and rich libraries such as Pandas and NumPy make it indispensable for data manipulation and analysis. R excels in statistical computing and visualizations, while SQL is essential for managing and querying databases efficiently. Beyond language proficiency, practical experience through projects and challenges is invaluable for honing skills in real-world scenarios. Continuous learning and staying updated with industry trends ensure proficiency in these essential tools for aspiring data scientists.

Similarly to get proficiency R, try using base functions and later packages such as dplyr, tidyverse for data manipulation and ggplot2 for advanced data visualization. Enhance skills with statistical modeling packages like caret and perform advanced regression analysis.

En fait, le langage R est un langage tout comme autre langage. C'est un moyen de communiquer avec la machine (le calculateur). On peut travailler avec dans plusieurs domaines, tel que l'analyse des données et la visualisation des données (data mining). A chaque fois je le rappelle à mes étudiants: "Le R n'est qu'une calculatrice, si vous ne connaissez pas la théorie, ou bien vous ignorez vos objectifs, ce n'est pas au R de le faire, c'est une machine bête est disciplinée, qui exécute ce que vous lui dites, donc pour parler à elle faut connaître sa langue, la lire, la parler et l'écrire". Je pense que le R est le meilleur dans l'analyse statistique des données et la visualisation, vu sa syntaxe facile, et les librairies open source.

I think if you want to be in web development as well as AI learning nodejs (MERN) as well as python (Flask) would be good option!

Although R is a software focused on statistical programming. Today there is a lot of progress in the development of packages for the development and deployment of apps in production, one of the companies that has very good packages in this area and that I recommend is appsilon

R excels in statistical analysis. Grasp its environment and data structures. Master packages like ggplot2 for stunning visuals and dplyr for data wrangling. Dive into Statistical Modeling with regression, hypothesis testing, and beyond. Practical projects are key to solidify your R skills and statistical intuition.

To master SQL queries, practice regularly by solving real-world problems, study advanced concepts like joins, subqueries, and indexing, and review and optimize your queries for performance.

SQL is a straightforward and accessible language for managing databases, making it easy to learn the basics like retrieving and updating data. However, truly mastering SQL involves understanding complex logic, such as combining multiple tables and handling intricate queries. This includes learning how to use JOINs to link tables and practicing advanced functions to solve real-world problems. As you delve deeper, optimizing your queries for better performance also becomes essential. With persistence and practice, you can effectively harness SQL to manage and analyze data efficiently, even in complex scenarios.

SQL is crucial for managing and querying large datasets stored in relational databases. Start with the fundamentals of SQL, including SELECT statements, JOIN operations, and aggregate functions. Practice by writing queries on sample databases or using platforms like SQLZoo and LeetCode. Understanding how to optimize queries for performance and integrating SQL with Python or R for seamless data workflows will significantly boost your data science capabilities.

SQL é a linguagem padrão para se trabalhar com consultas em banco de dados. Embora seja aparentemente fácil de usar e versátil, permitindo que qualquer pessoa com noções básicas de programação possa aprendê-la rapidamente, consultas SQL mal elaboradas podem causar sérios problemas de performance. Em grandes bancos de dados, essas consultas podem demorar muito para serem executadas, consumindo excessivamente os recursos do servidor e consequentemente afetando o desempenho geral do sistema. Consultas SQL complexas e mal escritas são difíceis de manter e depurar, aumentando o tempo e esforços necessários para solucionar problemas. Para escrever código SQL eficiente, é essencial conhecer técnicas e ferramentas de análise de desempenho.

To be a Data Science professional, master the fundamental and advanced SQL commands: SELECT and other basic commands, JOIN, subqueries, window functions, Common Table Expressions (CTEs), database design and normalization, practice in indexing and optimization of queries. If you have target projects on hand, ideally connected with real-world ETL processes, fuse SQL with popular tools like Python. Keeping pace with releases of new SQL standards is also good. Explore various versions of SQL like MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Oracle Database etc. Practice regularly on platforms like LeetCode and HackerRank and join communities for knowledge exchange and motivation.

Big Data tools are crucial for handling and analyzing vast datasets efficiently. Leverage Hadoop ecosystem tools, such as HDFS for storage and MapReduce for processing large-scale data. Similarly, Databricks built upon Apache Spark for distributed data computing, data processing and advanced analytics. During the learning process, following one cloud platform is better to quickly understand the tools, Azure or AWS or GCP. From my point-of-view Azure is good to start.

Apache Hadoop understand the Hadoop ecosystem (HDFS, MapReduce, YARN). Learn how to process large datasets in a distributed manner. Apache Spark Master its core concepts (RDDs, data frames, datasets) explore its capabilities for real-time processing, machine learning, and graph processing. combining a strong foundation in programming languages and big data tools can position yourself as a highly sought-after data scientist in the ever-evolving tech landscape.

To get into the Big Data Tools, here are a few steps that you can go through: 1. Python Basics: Master Python fundamentals (data types, operators, control flow, functions). 2. Hadoop: Understand HDFS (Hadoop Distributed File System) for storing massive data. Grasp MapReduce for batch processing large datasets. Learn YARN for resource management. 3. Spark: Explore RDDs (Resilient Distributed Datasets) for in-memory computations. Learn Spark's core APIs for SQL, machine learning, and streaming. Understand Spark's cluster architecture and job submission. 4. Practice: Work on real-world datasets and projects. Experiment with different tools and techniques. Remember to start small, focus on core concepts, and gradually expand your knowledge.

Harness the power of big data with Apache Hadoop and Apache Spark! Understand distributed computing and how these tools handle massive datasets. Dive deep into MapReduce and Spark's RDDs/DataFrames. Gain hands-on experience by working on real-world projects. Mastering these tools is crucial for any data scientist looking to tackle complex, large-scale data challenges. #bigdata #hadoop #spark #datascience #dataengineering

If you want to work with big data, it's very probable that you will also need to work on the cloud, so you need to understand about how this technology works, and to learn about a standard productive workflow. Take a look to Azure, Google Cloud, and test the power of the Cloud!

Master machine learning by diving into algorithms and their applications! Understand supervised, unsupervised, and reinforcement learning. Leverage libraries like scikit-learn or caret for implementation. Build models from scratch to grasp the nuances. Real-world projects will solidify your skills and enable you to solve complex problems. #machinelearning #datascience #algorithms #models #AI

Master machine learning by diving into algorithms and their applications! Understand supervised, unsupervised, and reinforcement learning. Leverage libraries like scikit-learn or caret for implementation. Build models from scratch to grasp the nuances. Real-world projects will solidify your skills and enable you to solve complex problems.

One very important thing to note while studying Machine Learning is that the field is built on the foundations of Linear Algebra, Calculus and Probability, so one has to make sure that the basics of these topics (eg vector spaces, linear transformation, standard normal distribution, maxima and minima etc.) are clear beforehand. Not only does it help understand the concepts in detail but also in understanding the variety of algorithms, optimizers etc available to you.

While courses and tutorials can help you learn the basics, participating in hackathons and competitions on platforms like Kaggle, Zindi or Devpost will equip you with in-demand and real-world Machine Learning skills you can hardly learn from courses.

Engaging in online courses and participating in R & Python communities and ML coding challenges like Kaggle can also accelerate your learning.

Follow Blogs and Journals: Regularly read data science blogs, research papers, and journals such as Towards Data Science, KDnuggets, and arXiv. Join Communities: Engage with online communities like GitHub, Stack Overflow, and Reddit’s r/datascience to discuss and learn from peers. Attend Conferences and Webinars: Participate in data science conferences, workshops, and webinars to network and learn about the latest advancements. Continuous Learning: Take advanced courses and certifications to keep up with new tools and techniques. Platforms like Coursera, edX, and Udacity offer specialized programs in data science and machine learning.

Additionally, keeping a Continuous Development Portfolio is helpful to document your learning and achievements, especially with the inclusion of reflective writing on each part of development. So when doing a course or a project on new technology, writing what you got out of it, reinforces learning and writing any further questions outstanding or problems can help guide the direction of learning.

The language that you will use is the less important, I mean, some companies or groups prefer R, others prefer Python. Sometimes will be important to use SQL (and its variations, for example: BigQuery) to pre-process your data and reduce the data processing. The most important is to be updated and know what toll is the best for each situation.