Best Data Science Courses & Certifications

 

Here’s a roadmap to become a Data Scientist:

1. Introduction to Python: Covers the basic concepts of Python programming language including variables, data types, control structures, functions, and modules.

2. Python Libraries: Covers important libraries for data analysis such as NumPy, Pandas, Matplotlib, and Seaborn.

3. Data Handling with Pandas: Covers reading, cleaning, and manipulating data using Pandas.

4. Data Visualization: Covers different types of data visualization using Matplotlib and Seaborn libraries.

5. Statistical Analysis: Covers statistical analysis including descriptive statistics, probability distributions, hypothesis testing, and regression analysis.

6. Machine Learning: Covers popular machine learning algorithms such as linear regression, logistic regression, decision trees, and random forests.

7. Real-life Applications: Covers practical applications of Python for data analysis such as web scraping, social media analysis, and image processing.

Overall, this roadmap provides a comprehensive approach to learn Python for data analysis, from the basics to advanced topics.

Also sharing important study resources along with their web links to pursue your dream of getting into the data science field. I will keep adding on to this list as I come across any valuable pages and people doing their best to democratize the data science field.

Andrew NG – Artifical Intelligence & Data Science

Krish Naik – Data Science

Sentdex – For Python

3Blue 1Brown – Mathematics for Data Science

StatQuest – Statistics & Machine Learning Math

Ben Eater – Computer Architecture, Electronics & Networking,

Industry Ready Data Science Courses

Data Science for Engineers – by IIT Madras (8 Week Program)

Scaler Neovarsity has an Industry-focused program with Real Business Case Studies. HIghly recognized and  You can use the following link & refferral code to get a discount of ₹10,000 on joining the course.

Joining Link

Unique referral code: PANK9BAC

ScalerTopics

Referral Code

Almabetter – Full Stack Data Science Course from AlmaBetter Academy.

NPTEL – Online Learning Initiatives by IITs and IISc
(Select Computer Science and Engineering under Discipline Box)

Database Management:

Azure Data Explorer (Kusto)

DSA (Data Structures & Algorithms):

CS50 by Harvard University

(An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently)

CS50 Lectures 2021 : Video Series on youtube

Open Source Society University

OSSU offers a comprehensive CS education online, for those seeking a well-rounded foundation and the discipline to learn largely on their own, with support from a global community.

 

Data Structures – GeeksforGeeks

Introduction to Algorithms by Thomas H Cormen

The Art of Computer Programming by Donald Knuth

Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People by Aditya Bhargava 

Data Structure Visualizations – by University of San Francisco

 

RPA: Robotics Process Automation Tools:

Automation Anywhere

UiPath

Power Automate & Power Apps

Project Management:

JIRA Fundamentals

Azure DevOps

BOOKS:

 

Computational Thinking: A Primer for Programmers and Data Scientists 

 

Automate the Boring Stuff with Python: by AI Sweigart (For Total Beginners)

 

Designing Data-Intensive Applications:
The Big Ideas Behind Reliable, Scalable, and Maintainable Systems  –  by Martin Kleppmann

Data Structures and Algorithms in Python (An Indian Adaptation) by Michael T. Goodrich

 

 

 

Data Domain Jobs & Roles

There are a variety of data-related job profiles with different skill requirements. Below are some of the most common data-related job profiles and the skills required for each:

1. Data Analyst: A data analyst is responsible for analyzing large sets of data to identify patterns and trends that can help businesses make informed decisions. Required skills include:

• Proficiency in statistical analysis and data visualization tools such as Excel, R, and Tableau.
• Knowledge of SQL for data querying and manipulation.
• Understanding of data cleaning, data normalization, and data transformation techniques.
• Familiarity with data modeling and data warehouse concepts.
• Strong problem-solving skills and attention to detail.

2. Data Scientist: A data scientist is responsible for using advanced analytics techniques to develop predictive models and drive strategic decision-making. Required skills include:

• Proficiency in statistical programming languages such as R and Python.
• Familiarity with machine learning algorithms and techniques.
• Understanding of data mining and predictive modeling techniques.
• Knowledge of data visualization and reporting tools such as Tableau and Power BI.
• Strong problem-solving skills and ability to communicate technical findings to non-technical stakeholders.

3. Business Intelligence Analyst: A business intelligence analyst is responsible for analyzing business data to provide insights into performance and inform decision-making. Required skills include:

• Proficiency in SQL and data visualization tools such as Tableau and Power BI.
• Understanding of data warehousing and business intelligence concepts.
• Knowledge of data modeling and ETL processes.
• Familiarity with data governance and data quality best practices.
• Strong communication and collaboration skills to work with business stakeholders.

4. Data Engineer: A data engineer is responsible for designing and implementing data pipelines to move and transform data from various sources. Required skills include:

• Proficiency in programming languages such as Python, Java, and Scala.
• Knowledge of distributed computing frameworks such as Hadoop and Spark.
• Understanding of data modeling and ETL processes.
• Familiarity with cloud computing platforms such as AWS and Azure.
• Strong problem-solving skills and attention to detail.

5. Data Architect: A data architect is responsible for designing the overall data architecture for an organization, including data modeling, data integration, and data security. Required skills include:

• Proficiency in data modeling tools such as ERwin, Toad Data Modeler and Visio.
• Understanding of data integration and ETL processes.
• Knowledge of data governance and data security best practices.
• Familiarity with cloud computing platforms such as AWS and Azure.
• Strong communication and collaboration skills to work with business stakeholders.

Overall, data-related job profiles require a combination of technical and non-technical skills, including proficiency in programming languages, statistical analysis, data visualization, and problem-solving, as well as strong communication and collaboration skills to work with business stakeholders,Toad Data Modeler.

Mastering the Fundamentals: A Guide to Data Analytics and Data Science Career for Students and Professionals

The interpretation of statistics, Python, models, and domain knowledge in relation to data analytics and data science is a common one. While there are certainly nuances and varying definitions within the field, this framework can be a helpful starting point for understanding the skills and knowledge required to work in these areas.

In this article, we will dive deeper into this framework and explore the importance of each component in data analytics and data science. We will also cover some of the key tools and techniques used in these fields and provide tips for those looking to pursue a career in data.

Statistics

Statistics is a branch of mathematics that deals with the collection, analysis, and interpretation of data. In data analytics and data science, statistics is a fundamental component as it provides the foundation for understanding and making sense of data. This includes concepts such as probability, hypothesis testing, and regression analysis, which are used to identify patterns and relationships within datasets.

Data Analytics = Python + Statistics

Python is a popular programming language that is widely used in data analytics and data science. It is known for its simplicity, versatility, and wide range of libraries that make it easy to work with data. When combined with statistics, Python becomes a powerful tool for analyzing and visualizing data.

In data analytics, Python is used to perform exploratory data analysis, which involves looking for patterns, trends, and anomalies within data. This is done using techniques such as data visualization, descriptive statistics, and data cleaning.

Machine Learning = Python + Statistics + Models

Machine learning is a subset of artificial intelligence that involves building models that can learn from data and make predictions or decisions. This is achieved by using statistical algorithms and mathematical models to identify patterns and relationships within data.

In addition to statistics and Python, machine learning requires a deep understanding of algorithms and models such as decision trees, neural networks, and support vector machines. These models are trained using historical data and can then be used to make predictions or classify new data.

Data Science = Python + Statistics + Models + Domain Knowledge

Data science is an interdisciplinary field that involves using data to solve complex problems and make data-driven decisions. In addition to statistics, Python, and machine learning, data science also requires domain knowledge, which is the understanding of the industry or subject matter being analyzed.

Domain knowledge can be acquired through education, work experience, or research. It is important because it allows data scientists to ask the right questions, identify relevant data sources, and make informed decisions based on the results of their analyses.

Tips for Pursuing a Career in Data Analytics and Data Science:

1. Learn the Fundamentals: To be successful in data analytics and data science, it is important to have a solid understanding of statistics and programming concepts. This includes probability, hypothesis testing, data structures, and algorithms.

2. Develop Technical Skills: In addition to the fundamentals, it is important to develop technical skills in data analysis, data visualization, and machine learning. This can be done through online courses, tutorials, or hands-on experience.

3. Build a Portfolio: To demonstrate your skills to potential employers, it is important to build a portfolio of projects that showcase your abilities. This can include personal projects or contributions to open source projects.

4. Network: Building connections within the data community can be helpful for finding job opportunities and staying up-to-date with the latest trends and technologies. Attend conferences, join online communities, and connect with others in the field.

5. Stay Curious: Finally, it is important to stay curious and continue learning. Data analytics and data science are rapidly evolving fields, and it is important to stay up-to-date with the latest tools and techniques.

In conclusion, understanding the role of statistics, Python, models, and domain knowledge in data analytics and data science is essential for anyone looking to pursue a career in these fields.

1. Descriptive statistics + Probability = Data Exploration
2. Inferential statistics + Hypothesis testing = Statistical Inference
3. Linear Algebra + Multivariate Calculus = Machine Learning Fundamentals
4. Optimization + Regression = Predictive Modeling
5. Time Series Analysis + Bayesian Inference = Forecasting
6. Graph Theory + Network Analysis = Data Visualization

The Power of Statistics in Data Analytics, Machine Learning, and Data Science

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It provides tools and methods to help researchers and data analysts draw conclusions from data and make decisions based on those conclusions. The principles of statistics can be applied to a wide range of fields, from biology and medicine to social sciences and business.

In the world of data analytics, statistics plays a crucial role in helping organizations make informed decisions. By analyzing data and identifying patterns, statisticians can help businesses identify trends, predict outcomes, and optimize their operations. Statistical methods are also used in quality control, where they help to detect defects in products and processes.

However, statistics alone is not enough for data analytics. In addition to statistics, data analysts need to be proficient in programming languages like Python. Python is a popular programming language in data analytics because it provides a powerful set of tools for working with data. With its libraries like NumPy, Pandas, and Matplotlib, Python makes it easy to analyze and visualize large data sets.

The combination of statistics and Python gives rise to data analytics. Data analytics involves the use of statistical methods and programming languages to analyze and interpret data. It helps organizations to identify patterns, trends, and insights that can help them make better decisions.

However, data analytics is not the end goal. The ultimate goal is to use data to create predictive models that can be used to make accurate predictions about future events. This is where machine learning comes in.

Machine learning is a type of artificial intelligence that uses statistical methods to enable machines to learn from data, without being explicitly programmed. In machine learning, models are created using algorithms that can identify patterns and make predictions based on those patterns.

When machine learning is combined with statistics and domain knowledge, we arrive at data science. Data science is the interdisciplinary field that combines statistical and computational techniques with subject matter expertise to extract insights and knowledge from data. A data scientist uses statistical techniques to analyze and interpret data, uses programming languages to manipulate and visualize data, and combines domain knowledge to create models that can be used to make predictions about future events.

In conclusion, statistics is a crucial component of data analytics, machine learning, and data science. Without statistics, it would be impossible to make sense of large amounts of data. However, statistics alone is not enough. To succeed in the world of data analytics, one needs to be proficient in programming languages like Python, and have domain knowledge to create models that can make accurate predictions. By combining statistics, programming, and domain knowledge, data scientists can create models that can help organizations make informed decisions and stay ahead of the competition.