Road map to Data Science specialization
What is Data Scicnce ?
- “…a field that deals with unstructured, structured data, and semi-structured data. It involves practices like data cleansing, data preparation, data analysis, and much more.
- Data science is the combination of statistics, mathematics, programming, and problem-solving; capturing data in ingenious ways; the ability to look at things differently; and the activity of cleansing, preparing, and aligning data.”
Python (OOP + Pandas + Numpy + Scipy)
Data Cleaning: One of the MOST important skills that you need to master to become a good data scientist, you need to practice on many datasets to master it.
Data Visualization (using Matplotlib and seaborn )
SQL and DB (SQL for Data Analysis)
Time Series Analysis
Below is recomneded cources from prestigious universities
Python for Everybody Specialization (5 Cources)
Begin your journey into the world of programming with this beginner-level program, which offers comprehensive knowledge about Python, data structures, APIs, databases, data retrieval, and visualization. Develop your own data-driven applications as you learn from experts at the prestigious University of Michigan.
Statistics with Python Specialization (3 Cources) from University of Michigan
Experience a practical yet modern approach to statistical thinking in this beginner program. Learn how to create and interpret data visualizations, apply and interpret inferential procedures, utilize various statistical modeling techniques, and understand the significance of linking research questions to data analysis methods, all using Python.
Learn SQL Basics for Data Science Specialization from UCDavis University (4 Cources)
Begin your data science journey with this beginner's program, designed to teach you the basics of SQL for data analysis and manipulation. You'll learn how to utilize SQL commands, create and assess datasets, use a collaborative Databricks workspace, and develop proposals, enhancing your ability to analyze and explore data efficiently.
Databases and SQL for Data Science with Python from IBM (needed SQL knowledge )
Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts and Data Engineers. Much of the world's data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases.
In this course you will learn SQL inside out- from the very basics of Select statements to advanced concepts like JOINs. You will: -write foundational SQL statements like: SELECT, INSERT, UPDATE, and DELETE -filter result sets, use WHERE, COUNT, DISTINCT, and LIMIT clauses -differentiate between DML & DDL -CREATE, ALTER, DROP and load tables -use string patterns and ranges; ORDER and GROUP result sets, and built-in database functions -build sub-queries and query data from multiple tables -access databases as a data scientist using Jupyter notebooks with SQL and Python -work with advanced concepts like Stored Procedures, Views, ACID Transactions, Inner & Outer JOINs Through hands-on labs and projects, you will practice building SQL queries, work with real databases on the Cloud, and use real data science tools. In the final project you’ll analyze multiple real-world datasets to demonstrate your skills.
Data Visualization with Tableau Specialization from UCDavis University (5 Cources )
Explore the world of data visualization through the lens of Tableau in this beginner-friendly program, designed to turn data into insightful, high-impact visualizations. Learn to effectively assess data quality, design custom dashboards, and deliver compelling narratives that facilitate data-driven decision making in any business scenario.
An Intuitive Introduction to Probability from university of Zurich ()
This course will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox" of methods to deal with uncertainty in your daily life.
The course is split in 5 modules. In each module you will first have an easy introduction into the topic, which will serve as a basis to further develop your knowledge about the topic and acquire the "tools" to deal with uncertainty. Additionally, you will have the opportunity to complete 5 exercise sessions to reflect about the content learned in each module and start applying your earned knowledge right away. The topics covered are: "Probability", "Conditional Probability", "Applications", "Random Variables", and "Normal Distribution". You will see how the modules are taught in a lively way, focusing on having an entertaining and useful learning experience! We are looking forward to see you online!
Sequences, Time Series and Prediction from Deep Learning.AI
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
An All-in-one Cource
Data Science Fundamentals with Python and SQL Specialization 5 cources from IBM
Step into the thriving field of data science, laying a solid foundation with essential tools such as Python, SQL, and statistics. This beginner program synthesizes a hands-on approach with real world data sets, preparing learners for tasks related to data analysis, querying databases, and more.
3) Data Scicnce
**Udacity offers a variety of benefits to its students, including:
- Accredited Nanodegree programs: Udacity's Nanodegree programs are accredited by Southern Association of Colleges and Schools Commission on Colleges (SACSCOC). This means that the programs meet the same quality standards as traditional university degrees.
- Industry-aligned curriculum: Udacity's Nanodegree programs are designed in collaboration with industry leaders to ensure that students learn the skills that are in demand.
- Personalized learning: Udacity's Nanodegree programs are self-paced, so students can learn at their own pace. However, students also have access to mentors and coaches who can help them stay on track.
- Hands-on projects: Udacity's Nanodegree programs require students to complete hands-on projects. This helps students apply the skills they learn in the real world.
- Career services: Udacity offers career services to help students find jobs after they complete their Nanodegree programs. These services include resume reviews, mock interviews, and job placement assistance.
In addition to these benefits, Udacity also offers a variety of discounts and financing options to make its Nanodegree programs more affordable.
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