[00:01] there are nine essential skills you need to master let's dive in and check them to master let's dive in and check them out one by and interpreting complex data to provide actionable insights as a data scientist
[00:16] you'll need to get good at a bunch of different skills from programming and math to data handling and visualization let's jump in first up you need to get the hang of python it's the main language in data science and it's pretty
[00:28] easy to pick up you can get a decent grasp of it in about a month or two then there's R it's another popular language in data science start with python because it's super versatile and once you're comfortable with it you can dive
[00:41] into R for its cool statistical and visualization features next you need to learn git git isn't a programming language it's a Version Control System we use to track changes to our code and collaborate with others git has a ton of
[00:54] features but you don't need to learn them all think of it like the 8020 rule 80% of the time you use 20% of GS features 1 to two weeks of practice is enough to get up and running next you need to dive into data structures and
[01:08] algorithms understanding these Concepts will boost your problem solving skills which is key for tackling complex challenges plus speak take companies like Google Amazon and Facebook always ask about them in job interviews spend
[01:21] about a month or two on this and you'll be in great shape next you need to get comfortable with SQL SQL stands for structured query language it's a very very simple language we use for working with databases as a data scientist you
[01:34] should know how to use SQL to access organize and analyze the data you need SQL is pretty simple and you can get a decent grasp of it in about a month or two by the way to help you on this journey I've created a free
[01:47] supplementary PDF that breaks down the specific Concepts you need to learn for each skill it's a great resource to review your progress find gaps your knowledge and prepare for interviews you can find the link in the description box
[01:59] also I have a bunch of tutorials on this channel and complete courses on my website if you're looking for structured learning again links are in the description box next you need a solid foundation in mathematics and statistics
[02:12] this is crucial because data science relies heavily on these principles focus on linear algebra calculus probability and statistics these will help you understand data analysis techniques and how to interpret data correctly spend
[02:26] about 2 to 3 months mastering these topics I'm after that you need to get good at preparing and visualizing data this means cleaning up the data and organizing it in a way that makes it easy to analyze you'll need to Learn
[02:39] Python libraries like pandas and numpy to manipulate and clean the data once it to understand patterns and communicate results libraries like matte plot lip and Seaborn help you create insightful visualizations to identify
[02:55] Trends and anomalies also why not strictly necessary getting familiar with business intelligence tools like Tableau or powerbi can give you an edge because these tools are widely used in the industry for creating interactive and
[03:09] sharable dashboards if you already have a solid background in Python and SQL you can get a good grasp of data pre-processing and visualization in a a handle on machine learning fundamentals machine learning algorithms
[03:24] fall into two categories supervised and unsupervised in supervised learning the model learns from labeled data meaning each input comes with a known output in unsupervised learning the model works with unlabel data and tries to figure
[03:39] out patterns and relationships on its own it's important to learn about these types of algorithms and how they work you'll also need to get familiar with tools like tensor flow pytorch and psyit learn which are used to build and train
[03:53] machine learning models dedicate about 3 to 4 months to master the core machine learning Concepts and how to use the these tools effectively after you've got the basics of machine learning down it's time to explore deep learning deep
[04:06] learning is a subset of machine learning that uses neural networks with many layers that's why we call it deep these networks are great for handling more complex tasks like image and speech recognition start with understanding the
[04:19] basics of neural networks and then move on to more advanced architectures like cnns and rnns tools like tensor flow and Pie torch are also essential here spend about 2 to 3 months getting a good grasp of deep learning Concepts and how to
[04:33] implement them all right at this point youve got a solid foundation in data science it's like you have become a general doctor now doctors often specialize in different areas like the heart or kidneys similarly you should
[04:45] consider specializing in exciting Fields like natural language processing also called NLP or computer vision NLP is all about working with text and language data it's used for things like analyzing sentiments in text translating languages
[05:00] and creating chat pods like chat GPT computer vision on the other hand is about teaching computers to understand and interpret visual data like images and videos it's used in facial recognition detecting objects and even
[05:13] in self-driving cars now you don't need to learn them both unless you're very enthusiastic so pick the one that interests you the most spend two to 3 months diving deep into one of these areas now as you move forward in your
[05:25] time when you need to work with massive data sets that's where Big Data comes in Big Data is all about handling and processing huge amounts of data quickly tools like Hadoop and Spark are super
[05:38] handy for this spend 2 to 3 months getting the hang of these tools and you'll be able to spot patterns and trends that you wouldn't catch with smaller data sets so if you dedicate 3 to 5 hours every day you can follow this
[05:52] road map and pick up all the skills you need to apply for an entrylevel data science job in about 12 to 20 months if you have any questions please let me know in the comments below and I will do my best to answer you right here or in
[06:04] my future videos if you enjoy this video please give it a like And subscribe for please give it a like And subscribe for more useful content thanks for watching