Data Science

Mastering Guide for Data Science Aspirants: Where to Start?

Introduction

Data science originated by combining various fields like Mathematics, Statistics, Computer Science, and Business. Data professionals write algorithms and build models based on the collected data. Data is collected from various private and public data set repositories like healthcare, finance, transportation, and education. Data can be used to diagnose diseases, capture patient records, optimize traffic flow, and improve public safety. Data professionals use this massive amount of data to gain insights and make predictions.

Data Science Roadmap

Data Science Roadmap
  1. Build Programming Skills
  1. Python
# Example to print Hello, world! on screen

def hello_world():
  
  # This function prints "Hello, world!"

  print("Hello, world!")

if __name__ == "__main__":
	hello_world()
  1. R
# This program will print the Fibonacci sequence up to the 10th term.

fibonacci <- function(n) {
  if (n == 0) {
    return(0)
  } else if (n == 1) {
    return(1)
  } else {
    return(fibonacci(n - 1) + fibonacci(n - 2))
  }
}

for (i in 1:10) {
  print(fibonacci(i))
}

Output:

0          1          1          2          3          5          8          13       21       34

  1. SQL
SELECT * FROM Employees WHERE Name = 'Basit';

This query will select all the rows from the Employees table where the name is Basit. The * symbol in the SELECT clause is used to select all columns from the Employees table.

  1. Firm Grip on Mathematics and Statistics
  1. Data Analysis and Visualization

Data analysis and visualization are both mandatory skills in a data science career. Data analysis involves the process of inspecting, cleaning, manipulating, and modeling data. It is also used to measure patterns and identify the latest trends in data. Various types of data analysis include descriptive, predictive, prescriptive, and diagnostic analytics. A person who analyzes and gains insights from data is called a data analyst. Data analysts use various tools like Power BI and Tableau for data analysis.

Data visualization is a powerful tool for graphical representation of data, and it involves using tools like Python, R, and Matplotlib. Strong data visualization skills are crucial for succeeding in a data science career. Some benefits of using data visualization include improved communication, better engagement, and gaining deeper insights.

Data Visualiation
  1. Best Data Science Tools
  • Jupyter Notebook
Jupyter Notebook

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and text. The Jupyter Notebook is the original web application for creating and sharing computational documents. It offers a simple, streamlined, document-centric experience.

  • SQL

SQL is a structured query programming language used to manage records in relational database management systems. It performs a variety of tasks, including extracting, manipulating, and analyzing data.

  • Pandas
Pandas example
  • Microsoft Excel

MS Excel is a powerful visualization tool used in data science for the manipulation and analysis of data. It provides various tools like Power Query, pivot tables, statistical analysis, and data visualization. However, it has limitations with complex datasets.

MS Excel
Data Transformation in Microsoft Excel
  • Tableau

Tableau is an extensively used data visualization software for creating interactive dashboards and visualizations. It is a powerful tool to gain insights from data. Data scientists use it to visualize complex data.

Solar Energy Dashboard
Tableau Solar Energy Dashboard
  • Power BI

Another great business analytics tool developed by Microsoft is Power BI. It is used for the visualization, cleaning, transformation, exploration, modeling, and reporting of data. Power BI is a very useful tool as it utilizes the DAX (Data Analysis Exploration) language, where data scientists define specific metrics and perform state-of-the-art calculations.

powerbi
Power BI Dashboard
  • Scikit-learn

Scikit-learn is a powerful Python library used in data science and machine learning. It provides various tools to build machine learning models, making it a top choice for data professionals to analyze and create machine learning models.

  • Tensor Flow
  • Apache Spark

Apache Spark efficiently manages big data and provides a platform for bulk processing and writing queries. Data professionals use it to run large-scale machine learning experiments and handle complex analyses to derive insights from datasets.

  • Mongo DB

MongoDB is a popular NoSQL (Non-Structured Query Language) database used in data science. MongoDB stores data in the form of binary JSON, which is best suited for handling unstructured data in data science tasks. It is often used in conjunction with pandas, NumPy, and sci-kit-learn. MongoDB allows data to be stored without a predefined schema. It is highly scalable, meaning data can be shared among various servers to handle large traffic. MongoDB is more flexible in handling unstructured data as compared to SQL.

  • Elasticsearch

Elasticsearch is a search engine used for searching large datasets. It offers real-time data analysis, document analysis, and time series analysis. Highly scalable, it efficiently manages complex tasks and extracts insights from data.

  1. Machine Learning

Machine learning is used to find and debug errors in data by applying automation techniques. It is utilized for data cleaning, analysis, and building state-of-the-art models. The two most common types of machine learning are supervised and unsupervised learning.

Supervised learning is a type where the model receives training on labeled data. For example, a dataset could be trained on patients who have already been diagnosed with a disease. The model will then predict all the possible diseases based on a patient’s symptoms.

Unsupervised learning, on the other hand, is a type where the model receives training on unlabeled data. For instance, a clustering model could be used to group text documents based on their similarities. It identifies similarities between text clusters and groups all documents together.

Some of the most popular machine learning models include linear regression, logistic regression, support vector machines (SVM), decision trees, random forests, and neural networks.

  1. Deep Learning

Deep learning is a subcategory of machine learning that learns from data using artificial neural networks. PyTorch and TensorFlow are the most popular Python libraries for deep learning models. It is a powerful tool that provides various tasks, including image recognition, natural language processing (NLP), speech recognition, medical diagnosis, financial forecasting, and more. Deep learning is a rapidly evolving field, with new applications being built increasingly every day.

Deep learning models offer a high level of accuracy and scalability. However, there are also some challenges, such as data requirements, computational resources, and difficulties in interpretation. Some of the most popular models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), autoencoders, and generative adversarial networks (GANs).

  1. Computer Vision

Computer vision is a branch of deep learning that deals with the extraction of useful information from images and videos. It is used in a wide variety of applications such as image recognition, NLP, speech recognition, medical diagnosis, and financial forecasting. Computer vision models have a high level of accuracy, are scalable to handle large datasets, and are easier to interpret than traditional machine learning models.

Various models used in computer vision include convolutional neural networks (CNNs), recurrent neural networks (RNNs), support vector machines (SVMs), decision trees, and random forests.

  1. Natural language processing

Natural language processing (NLP) is an emerging field of computer science that deals with human-computer interaction (HCI) based on natural languages. It is used in a variety of applications, including text summarization, sentiment analysis, chatbots, machine translation, and more. NLP is highly scalable and can handle large datasets. However, it requires large datasets to train, is difficult to interpret, and is computationally expensive to train and deploy.

Data Science for Beginners: Real-World Projects

Extraction and gaining insights from data is a very challenging task, which requires a complete set of skills. By leveraging real-world projects, one could gain access to ultimate knowledge and build a strong portfolio.

A list of various real-world projects is as follows: (Click on resources to view the projects)

Sr. No.ProjectResource Link
1.Sentiment Analysis1. Amazon Reviews Dataset
2. Amazon Reviews dataset
3. Twitter Sentiment analysis – Medium
4. Twitter Sentiment analysis – analytics vidhya
2.Fake News detectionDetecting Fake News
3.Detecting Parkinsons’s Disease1. Disease detection using XGBoost
2. Pyimagesearch – Detecting Parkinsons’s Disease
4.Color DetectionOpenCV Project
5.Iris Data Set – Predict the class of the FlowerMany – analytics vidhya
6.Loan Prediction – Predict if a loan will get approved
or not.
Many – analytics vidhya
7.BigMart Sales Dataset – Predict the sales of a store.Many – analytics vidhya
8.House Price RegressionKaggle
9.Wine quality – Predict the quality of the wine.Kaggle kernel
10.Heights and Weights Dataset – Predict the height or weight of a person.Study of height versus weight
11.Email ClassificationYoutube
12.Titanic dataset1. Comprehensive data exploration with Python- Kaggle
2. Titanic Data Science Solutions – Kaggle
3. Data Science Tutorial for Beginners – Kaggle
4. Introduction to Ensembling/Stacking in Python – Kaggle
5. A Data Science Framework: To Achieve 99% Accuracy – Kaggle
6. Stacked Regressions : Top 4% on LeaderBoard – Kaggle
7. An Interactive Data Science Tutorial – Kaggle
8. EDA To Prediction(DieTanic) – Kaggle
9. Titanic: Machine Learning from Disaster – Kaggle

Soft Skills for Data Science Aspirants

  1. Communication skills
  2. Problem-solving skills
  3. Critical thinking and logic
  4. Collaboration and teamwork
  5. Domain knowledge
  6. Time management
  7. Business intelligence
  8. Presentation skills
  9. Storytelling

6 thoughts on “Mastering Guide for Data Science Aspirants: Where to Start?”

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