Data Science is one of the hottest topics of the 21st century that must know. If you have decided to kickstart your data science journey then this article will help you to understand the core concepts of Data Science. Data Science is the science of analyzing uncleaned data for valuable purposes with the help of statics and machine learning algorithms.
This article provides all the information needed to create a data science roadmap for 2023. We explain what a data science roadmap is, the various components and achievements included in a data science roadmap, and track your progress along a data science roadmap and sources of other related power.
What is Data Science?
Data science is a solid and rapidly growing field with a lot of untapped potentials. Data science is the science of analyzing raw data using statistics and machine learning techniques to make inferences about that information.
Why Data Science?
Data science turns raw data into meaningful insights. Therefore, the industry needs data science. Data scientists are magicians who know how to work wonders with data. A skilled data scientist knows how to extract meaningful information from any data they find. He helped the company in the right direction. The company needs a robust data-driven solution where he is an expert. A data scientist is an expert in various core areas of statistics and computer science. He uses his analytical skills to solve business problems.
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Data Science Roadmap
Step.1: Programming Language
If you want to be a Data Scientist then you will have to learn one of the Programming languages like Python or R. personally I will recommend you to learn Python Programming because Python is used in many fields and because of its simple structure. Programming Language will help you to understand the structure of concepts or code. You can clean, visualize or analyze any data with the help of one programming language in data science. So here I have given a list of programming languages that you can consider learning.
List of Programming Languages of Data Science:
- Python
- R
- SAS
- Matlab
Step.2: Database for Data Science
Now the second thing is that you will have to know about databases. With the increasing amount of data, in addition to SQL databases, NoSQL databases are also available, which can store unstructured data. If you know basic SQL queries, you can easily switch to any database and you will have to learn databases for data Science.
List of Database Tools:
- SQL
- MongoDB
- PostgreSQL
- CouchDB
- Cassandra
- MySQL
Step.3: Mathematics
Data science is about applying math to data. If you don't understand math, you will struggle with data science. You can start learning data science math with Numerical Python and statistics. After learning the basics of programming, it's a good idea to brush up on your statistics. You can never be a good data scientist if you don't know the statistics Matrix. Statistics and Matrix is at the core of data science or any other field that deals with data. Statistics, Matrix, and Calculus help you understand, analyze, gain insights and draw conclusions from data.
List of Mathematics Concepts:
- Probability
- Statistics
- Linear Algebra
- Calculus
- Analytic Geometry
- Matrix
- Vector Calculus
- Optimization
- Regression
- Dimensionality Reduction
- Density Estimation
- Classification
Step.4: Learn Data Analytics
Both data analysts and data scientists work with data, with the main difference being what they do with the data. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations that help companies make more strategic decisions. Meanwhile data scientists design and create new processes to model and generate data using prototypes, algorithms, predictive models, and custom analytics.
List of Data Analytics Tools:
- Google Sheet
- Pandas
- Excel
- SQL
- Python
- SciPy
- Web Scraping
- Selenium
- Numpy
- Matplotlib
Step.5: Machine Learning
Machine learning involves theory (the math behind ML algorithms) and practice (applying ML algorithms to problems using libraries). The theoretical part is just as important as it helps you understand which algorithm can perform better, how to optimize the model, and how to evaluate the model's performance.
List of ML Concepts:
- Supervised learning
- Regression
- Classification
- Unsupervised learning
- Clustering
- Association
- Reinforcement learning
- Regularisation
- Bias-variance Tradeoff
- Continuous Target Variables
- Discrete Target Variables
- Gradient Descent
- Representation Learning
- Curse of dimensionality
Step.6: Deep Learning
Deep learning includes neural networks, natural language processing, and computer vision. Learning basic neural networks, which include forward and backward propagation, various loss functions, etc. is a must. You can choose NLP or Computer Vision depending on whether you are interested in working with text or images.
Here is the List of Deep Learning Concepts:
- Cost Function
- Activation Functions
- Backpropagation
- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Hyper-parameters
- A Single Neuron
- Deep Neural Network
- Stochastic Gradient Descent
- Overfitting and Underfitting
- Dropout Batch Normalization
- Binary Classification
- Long Short-Term Memory Networks (LSTM)
Step.7: Learn Big Data
Big Data is a subset of Data Science which is a technique to collect, maintain and process huge information. It is a collection of data that is very large in volume but grows exponentially over time. This is data so large and complex that no traditional data management tool can store or process it efficiently so we used to use Big Data in place of a data management tool.
Big Data Tools:
- Hadoop
- MapReduce
- Spark
- Flink
Step.8: Natural Language Processing
Natural language processing (NLP) is a field of data science that studies how computers and languages interact with each other. The goal of NLP is to program computers to understand human language as it is used.
List of NLP Concepts:
- Named Entity Recognition (NER)
- Text Classification
- Word Vectors
- Tokenization
- Stemming and Lemmatization
- Stop Words Removal
- TF-IDF
- Keyword Extraction
- Sentiment Analysis
- Topic Modelling
Step.9: Data Visualization and Reporting Tools
Data visualization is the process of presenting your data in a graphical format, understanding the conclusions, and explaining them to others. It comes in various forms like a bar, column, line and pie charts, etc. Humans are visual creatures, and it's hard to overstate the importance of data visualization. It's an underrated skill, but it's the most important so you must know these tools.
- Data cleaning and managing
- Data wrangler
- Vega
- Excel
- Spotfire
- Tableau
- Power BI
- R Markdown
Step.10: Deployment Tools
As a data scientist is a huge field, so you must deploy tools also that help you to understand how to deploy code using these tools. Here I have listed some basic tools that are used in data science to deploy code.
- AWS
- Microsoft Azure
- Heroku
- Google Cloud Platform
- DevOps, MLOps, AIOps
Step.11: Top Python Libraries for Data Science
I have listed the top Python Libraries that every data scientist should know. Basically, if you don't know what a library is, you can add something to Python that gives Python more functionality and that you can use easily without writing long code.
Here is the list of Top Python Libraries:
- Pandas
- NumPy
- SciKit-Learn
- SciPy
- Matplotlib
- TensorFlow
- Keras
- PyTorch
- Seaborn
- Scrapy
- BeautifulSoup
- Selenium
Step.12: Data Science Other Concepts
Here is the list of other concepts that you must know to start a career as a Data Scientist. You must learn these concepts before starting Your data scientist journey.
Here is the list:
- Data Structure
- Linux
- Git
- Model Planning
- Outliers
Conclusion:
In this article, We have discussed How to Become a Data Scientist in 2023. We have covered whole topics to become a Data Scientist. The Complete Roadmap of The Data scientist has been discussed, Now you can simply follow this article to read these concepts from google and youtube. I have provided all the topics that every data scientist must know to make a career in The Data Scientist.
You should also check out, Django Developer Roadmap, Python Developer Roadmap, C++ Complete Roadmap, Machine Learning Complete Roadmap, Data Scientist Learning Roadmap, R Developer Roadmap, DevOps Learning Roadmap, and Laravel Developer Roadmap.
Thank you for reading this blog. I wish you the best in your journey in learning and mastering Data Science.
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