Data science courses

datascience coures

datascience coures

Learning a new skill is always refreshes your mind and boosts towards your dream.  When it comes to the data science field, learning the new skills to keep you updated with the latest data science technologies will give you the pool of opportunities.

With the increase in MOOC courses, anyone can learn any skill they desire. But choosing the right course out of the pool of MOOC course is a time-consuming process , So we were presenting you the best courses in the data science field.

Below are the best data science specialization course. These specializations will contain  a series 5 -6 courses which start from the very basics to advanced concepts in that particular specialization. Generally, these specializations will take 5 to 6 months of week time to learn and will get you the certificate for each and individual course and a final specialization certificate.

If you like to learn on particular data science course you can find them from the individual courses section after the specializations section in this page. You can find any course in the data science field on this page. If  you haven't found any particular course please let us so we can add that too 🙂

Note: This page will be updated with latest data science courses all the time, better bookmark it.

Python Data Science Specializations

Data Science Specializations in R Programming Language

Data Science Specializations

Machine Learning Specializations

Big Data Specialization

Business Analytics Specialization

Data Analytics for Business Bootcamp

Specialization Link:

Data Analytics for Business Bootcamp

Courses in this specialization:

 

 

Data Science  Courses in Python Programming Language

Introduction to Data Science in Python

Course Link:

Introduction to Data Science in Python

What You Will Learn

  • Basics of python programming language.
  • Data manipulation and cleaning techniques using the pandas data science library.
  • Statistics primer,  various statistical measures in pandas dataFrames.

Applied Plotting, Charting & Data Representation in Python

Course Link:

Applied Plotting, Charting & Data Representation in Python

What You Will Learn:

  • Visualization basics, with a focus on reporting and charting using the matplotlib library.
  • Design and information literacy perspective.
  • Statistical measures translate into in terms of visualizations.

Applied Machine Learning in Python

Course Link:

Applied Machine Learning in Python

What You Will Learn:

  • How machine learning different than descriptive statistics.
  • Introduction to scikit-learn toolkit.
  • Supervised approaches for creating predictive models.
  • Task of data clustering , as well as evaluating those clusters.
  • Cross-validation, overfitting concepts.

Applied Text Mining in Python

Course Link:

Applied Text Mining in Python

What You Will Learn:

  • Text mining and text manipulation basics.
  • Overview of the nltk framework for manipulation text.
  • Searching for text, cleaning text and preparing text for use by machine learning process.
  • Text classification.

 

Applied Social Network Analysis in Python

Course Link:

Applied Social Network Analysis in Python

What Your Will Learn:

  • Introduce the learner to network modelling through the networkx toolset.
  • Model knowledge graphs and physical and virtual networks.
  • Graph theory and motivations for why we might model phenomena as networks.
  • Social networking analysis workflow problem identification through to the generation of insight.

Getting Started with Python

Course Link:

Programming for Everybody (Getting Started with Python)

 What You Will Learn:

  • The basics of programming computers using python.
  • Program from a series of simple instructions.
  • Chapters 1-5 of the textbook "Python for informatics" concepts.

Python Data Structures

Course Link:

Python Data Structures

What You Will Learn:

  • Introduce the core data structures of python programming language.
  • Basics of procedural programming and explore how the built-in- data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis.

Using Python to Access Web Data

Course Link:

Using Python to Access Web Data

What You Will Learn :

  • Scrape, parse, and read web data as well as access data using web APIs.
  • Chapter 11-13 of the textbook "Python for informatics" concepts.
  • These concepts include variables and expressions, conditional executions (loops, branching and try/excepts) functions.

Using Databases with Python

Course Link:

Using Databases with Python

What You Will Learn:

  • Basics of the structured Query Language(SQL) as well as basic database design for storing data as part of multi-step data gathering analysis, and processing effort.
  • Using SQLite3 as a database.
  • Web crawlers and multi-step data gathering and visualization processes.
  • Using D3.js library to do basic data visualization.

Python for Genomic Data Science

Course Link:

Python for Genomic Data Science

What You Will Learn:

  • Introduction to the python programming language and the ipython notebook.
  • Python Data structures , conditions, loops, functions, Biopython.

Machine Learning Foundations: A Case Study Approach

Course Link:

Machine Learning Foundations: A Case Study Approach

What You Will Learn:

  • Will get hands-on experience with machine learning from a series of practical case-studies.
  • Predict house prices based on house-level features.
  • Analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

Machine Learning: Regression

Course Link:

Machine Learning: Regression

What You Will Learn:

  • Predicting house prices will create models that predict a continuous value(Price) from input features (square footage, number of bedrooms and bathrooms, etc)
  • Describe the input and output of a regression model.
  • Estimate model parameters using optimization algorithms.
  • Describe the notion of sparsity and how LASSO leads to sparse solutions.

Machine Learning: Classification

Course Link:

Machine Learning: Classification

What You Will Learn:

  • Describe the input and output of a classification model.
  • Tackle both binary and multiclass classification problems.
  • Implement a logistic regression model for large-scale classification.
  • Analyze financial data to predict loan defaults.
  • Implement these techniques in python.

Machine Learning: Clustering & Retrieval

Course Link:

Machine Learning: Clustering & Retrieval

What You Will Learn:

  • Create a document retrieval system using k-nearest neighbors.
  • Identify various similarity metrics for text data.
  • Reduce computations in the k-nearest neighbor search by using KD-tress, produce approximate nearest neighbors using locality sensitive hashing.
  • Examine probabilistic clustering approaches using mixtures models.
  • Compare and contrast initialization techniques for non-convex optimization objectives.
  • Implement these techniques in Python.

Machine Learning: Recommender Systems & Dimensionality Reduction

Course Link:

Machine Learning: Recommender Systems & Dimensionality Reduction

 What You Will Learn:

  • Creating a collaborative filtering system.
  • Reduce dimensionality of data using SVD, PCA, and random projections.
  • Perform matrix factorization using coordinate descent.
  • Deploy latent factor models as a recommender system.
  • Handle the cold start problem using side information.
  • Examine a product recommendation application.
  • Implement these techniques in python.

Data Science Course in R Programming Language

Introduction to Probability and Data


Course Link:

Introduction to Probability and Data

What You Will Learn:

  • Introduces you to sampling and exploring data, as well as basics probability theory and Baye's rule.
  • Various types of sampling methods, and discuss how such methods can impact the scope of inference.
  • Data Analysis techniques including numeric summary statistics and basic data visualization.
  • Installing and using R and RStudio.

Inferential Statistics

Course Link:

Inferential Statistics

What You Will Learn:

  • Commonly used statistical inference methods for numerical and categorical data.
  • Perform hypothesis tests, interpret p-values, and report the results of your analysis.
  • Using R for lab exercises and final projects.

Linear Regression and Modeling

Course Link:

Linear Regression and Modeling

What You Will Learn:

  • Introduces simple and multiple linear regression models.
  • The relationship between variables in a dataset and a continuous response variable.
  • Fundamental theory behind linear regression and , through data examples.
  • Finding the relationship between the physical attractiveness of a professor and their student evaluation score using R and Rstudio.
  • Utilize regression models to examine the relationships between multiple variables, using the free statistical software R and Rstudio.

 

Bayesian Statistics

Course Link:

Bayesian Statistics

What You Will Learn:

  • Describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates.
  • Bayes' rule to transform prior probabilities into posterior probabilities.
  • Underlying theory and perspective of the bayesian paradigm.
  • Building models to eliciting prior probabilities to implementing in R.
  • Bayesian comparison of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.

 

The Data Scientist's Toolbox

Course Link:

The Data Scientist's Toolbox

What You Will Learn:

  • Introduction to the main tools and ideas in the data scientist's toolbox.
  • Overview of the data, Question, and tools that data analysts and data scientist work with.
  • Conceptual introduction to the ideas behind turning data into actionable knowledge.

 

R Programming

Course Link:

R Programming

What You Will learn:

  • Program in R and how to use R for effective data analysis.
  • Install and configure software necessary for a statistical programming environment.
  • Practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R function, debugging, profiling R code.

Getting and Cleaning Data

Course Link:

Getting and Cleaning Data

What You Will Learn:

  • Obtaining data from the web, from APIs from databases and from colleagues in various formats.
  • Basics of data cleaning and how to make data "Tidy".
  • Components of a complete data set including raw data, processing instructions, codebooks, and processed data.

 

Exploratory Data Analysis


Course Link:

Exploratory Data Analysis

What You Will Learn:

  • Essential exploratory techniques for summarizing data.
  • Techniques applied before formal modeling commences and can help inform the development of more complex statistical models.
  • Plotting systems in R as well as some of the basic principles of constructing data graphics.
  • Multivariate statistical techniques used to visualize high-dimensional data.

Reproducible Research

Course Link:

Reproducible Research

What You Will Learn:

  • Concepts and tools behinds reporting modern data analyses in a reproducible manner.
  • Literate statistical analysis tools ( which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results).

Statistical Inference

Course Link:

Statistical Inference

What You Will Learn:

  • Modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses.
  • Fundamentals of inference in a practical approach for getting this done.
  • Broad directions of statistical inference and use this information for making informed choices in analyzing data.

Regression Models

Course Link:

Regression Models

What You Will Learn:

  • Linear models, the most important statistical analysis tool in a data scientist's toolkit.
  • Regression analysis, least squares and inference using regression models.
  • ANOVA and ANCOVA
  • Analysis of residuals and variability.
  • Modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Practical Machine Learning

Course Link:

Practical Machine Learning

What You Will Learn:

  • Basic components of building and applying prediction functions with an emphasis on practical applications.
  • The basic grounding in concepts such as training and tests sets, overfitting, and error rates.
  • Introduce a range of model-based and algorithmic machine learning methods including regression, classification trees, naive Bayes, and random forests.
  • The complete process of building prediction functions including data collection feature creation, algorithms, and evaluation.

Developing Data Products

Course Link:

Developing Data Products

What You Will Learn:

  • Data products automate complex analysis tasks , technology to expand the utility of a data-informed model algorithm or inference.
  • The basics of creating data products using shiny, R packages, and interactive graphics.
  • The statistical fundamental of creating a data product that can be used to tell a story about data to a mass audience.

Big Data Courses

Introduction to Big Data

Course Link:

Introduction to Big Data

What You Will Learn:

  • Describe the Big Data landscape including examples of real world big data problems.
  • Explain the V's of Big Data (Volume, Velocity, Variety, Veracity, Valence and Value)
  • Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFC file system and the MapReduce.
  • Install and run a program using Hadoop.

Big Data Modeling and Management Systems

Course Link:

Big Data Modeling and Management Systems

What You Will Learn:

  • Recognize different data elements in your own work and everyday life problems.
  • Identify the frequent data operations required for various types of data.
  • Apply techniques to handle streaming data.
  • Design a big data information system for an online game company.

Big Data Integration and Processing

Course Link:

Big Data Integration and Processing

What You Will Learn:

  • Describe the connections between data management operations and the big-data processing patterns needed to utilize them in large-scale analytical applications.
  • Execute simple big data integration and processing on Hadoop and Spark platforms.

Machine Learning With Big Data

Course Link:

Machine Learning With Big Data

What You Will Learn:

  • Design an approach to leverage data using the steps in the machine learning process.
  • Apply machine learning techniques to explore and prepare data for modeling.
  • Construct models that learn from data using widely available open source tools.
  • Analyze big data problems using scalable machine learning algorithms on Spark

Graph Analytics for Big Data

Course Link:

Graph Analytics for Big Data

What You Will Learn:

  • Understand your data network structure and how it changes under different conditions.
  • Perform analytics tasks over that graph in a scalable manner.
  • Apply graph techniques to understand the significance of your data sets for your own projects.

Data Science Courses

A Crash Course in Data Science

Course Link:

A Crash Course in Data Science

What You Will Learn:

  • Identify different types of questions and translate them to specific datasets.
  • Describe different types of data pulls.
  • Explore data sets to determine if data are appropirate for a given question.
  • Integrate statistical findings to from coherent data analysis presentations.

Building a Data Science Team

Course Link:

Building a Data Science Team

What You Will Learn:

  • Roles in the data science team including data scientist and data engineer.
  • How the data science team relates to other teams in an organization.
  • Relevant questions for interviewing data scientists.
  • How to guide data science teams to success.

Managing Data Analysis

Course Link:

Managing Data Analysis

What You Will Learn:

  • Describe the basic data analysis iteration.
  • Identify different types of questions and translate them to specific datasets.
  • Explore data sets to determine if data are appropriate for a given question.
  • Integrate statistical findings to from coherent data analysis presentations.

Data Science in Real Life

Course Link:

Data Science in Real Life

What You Will Learn:

  • Describe the "perfect" data science experience.
  • Identify strengths and weaknesses in experimental design.
  • Challenge statistical modeling assumptions and drive feedback to data analysts.
  • Experimental design, randomization, A/B testing.

Business Analytics Courses

Business Metrics for Data-Driven Companies

Course Link:

Business Metrics for Data-Driven Companies

What You Will Learn:

  • Will learn best practices for how to use data analytics to make any company more competitive and more profitable.
  • Recognize the most critical business metrics and distinguish them from mere data.
  • You'll understand why the companies are so disruptive and how they use data analytics techniques to out-compete traditional companies.

Mastering Data Analysis in Excel

Course Link:

Mastering Data Analysis in Excel

What You Will Learn:

  • Use Excel to do all math formulas are given as excel spreadsheets.
  • Calculate and apply to real world examples the most important uncertainty measures used in business, including classification error rates, the entropy of information, and confidence intervals for linear regression.
  • To become fluent in its most commonly used business functions.
  • Practical knowledge of how to apply business data analysis methods based on binary classification.

Data Visualization and Communication with Tableau

Course Link:

Data Visualization and Communication with Tableau

What You Will Learn:

  • Become the master at communicating business-relevant implications of data analysis.
  • Structure your data analysis projects to ensure the fruits of you hard labor yield results for your stakeholders.
  • Streamline your analyses and highlight implications efficiently using visualizations in Tableau.
  • Designing and persuasively presenting business "data stories" that use these visualizations.

Managing Big Data with MySQL

Course Link:

Managing Big Data with MySQL

What You Will Learn:

  • How to use the relational database in business analysis.
  • Use entity-relationship diagrams to display the structure of the data held within them.
  • Execute the most useful query and table aggregation statements for business analysts, and practice using them with real databases.
  • Insights into how to improve businesses.

Increasing Real Estate Management Profits: Harnessing Data Analytics

Course Link:

Increasing Real Estate Management Profits: Harnessing Data Analytics

What You Will Learn:

  • Capstone project using data analysis to recommend a method for improving profits for your company.

Customer Analytics

Course Link:

Customer Analytics

What You Will Learn:

  • Describe the major methods of customer data used by companies and understand how this data inform business decisions.
  • Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool.
  • Communicate key ideas about customer analytics and how to filed informs business decisions.

Operations Analytics

Course Link:

Operations Analytics

What You Will Learn:

  • Impact the way you think about transforming data into better decisions.
  • Improvements in data-collecting technologies have changed the way firms make informed and effective business decisions.
  • How the data can be used to profitably match supply with demand in various business settings.
  • How to predict the outcomes of competing for policy choices and how to choose the best course of action in the face of risk.

People Analytics

Course Link:

People Analytics

What You Will Learn:

  • Deep analysis of data rather than the traditional methods of personal relationships.
  • Decision-making based on experience , and risk avoidance.
  • How data and sophisticated analysis is brought to bear on people -related issues, such as recruiting, performance evaluation, leadership, hiring and promotion , job design, compensation, and collaboration.

Accounting Analytics

Course Link:

Accounting Analytics

What You Will Learn:

  • How financial statement data and non-financial metrics can be linked to financial performance.
  • Accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insights.
  •  Explore many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization and more.

Database Management Essentials

Course Link:

Database Management Essentials

What You Will Learn:

  • Foundation you need for a career in database development, data warehousing, or business intelligence.
  • Relational databases, write SQL statements to extract information to satisfy business reporting request, create entity relationship diagrams (ERDs) to design databases.
  • Writing SQL statements in oracle or MySQL.

Data Warehouse Concepts, Design, and Data Integration

Course Link:

Data Warehouse Concepts, Design, and Data Integration

 What You Will Learn:

  • Exciting concepts and skills for designing data warehouses and creating data integration workflows.
  • You will have hands-on experience with data warehouse design and use open source products for manipulating pivot tables and creating data integration workflows.
  • Maturity models, architectures, multidimensional models, and management practices
  • Finally, you will create data warehouse designs and data integration workflows that satisfy the business intelligence needs of organizations.

Relational Database Support for Data Warehouses

Course Link:

Relational Database Support for Data Warehouses

What You Will Lean:

  • Use analytical elements of SQL for answering business intelligence questions.
  • Features of relational database management systems for managing summary data commonly used in business intelligence reporting.
  • Difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts.

Business Intelligence Concepts, Tools, and Applications

Course Link:

Business Intelligence Concepts, Tools, and Applications

What You Will Learn:

  • Knowledge and skills for using data warehouses for business intelligence purposes and for working as a business intelligence developer.
  • Opportunity to work with large data sets in a data warehouse environment and will learn the use of MicroStratgys online analytical processing (OLAP) and visualization capabilities to create visualization and dashboards.

Design and Build a Data Warehouse for Business Intelligence Implementation

Course Link:

Design and Build a Data Warehouse for Business Intelligence Implementation

What You Will Learn:

  • You'll design and build a small data warehouse, create data integration workflows to refresh the warehouse.
  • Write SQL statements to support analytical and summary query requirements and use the mincroStrategy business intelligence platform to create dashboards and visualizations.
  • Finally, you will use MicroStrategy OLAP capabilities to gain insights into your data warehouse.

Fundamentals of Visualization with Tableau

Course Link:

Fundamentals of Visualization with Tableau

What You Will Learn:

  • Understanding the data in a better way with better visualization.
  • Visualization fundamentals with Tableau interface.
  • Understanding the need of different tools in Tableau.
  • Loading the business data into Tableau and understanding the relationships and different analysis.

Essential Design Principles for Tableau

Course Link:

Essential Design Principles for Tableau

What You Will Learn:

  • Understand and practise the Tableau visualizations.
  • Introduce the fundamental concepts of data visualizations.
  • The key difference between exploratory and explanatory analysis.
  • Apply best pracitce design principles to you data visualization.

Visual Analytics with Tableau

Course Link:

Visual Analytics with Tableau

What You Will Learn:

  • Charting, dates, table calculations and mapping with Tableau.
  • Look at different charts like scatter plots, Gantt charts, histograms, bullet charts and many others.
  • When  to use discrete and continues dates to explain the data.
  • Creating custom and quick table calculations  with Tableau.

Creating Dashboards and Storytelling with Tableau

Course Link:

Creating Dashboards and Storytelling with Tableau

What You Will Learn:

  • Create dashboards with Tableau to tell the story of your data.
  • Learn more advanced features of Tableau these include hierarchies, actions, and parameters to guide user interactions.
  • Learn how to construct and organize your data story for maximum impact.

Data Visualization with Tableau Project

Course Link:

Data Visualization with Tableau Project

What You Will Learn:

  • A case study of a midsized company to run its supply chain more efficiently by balancing supply and demand.
  • Course registered learns will get the sample data to balance to companies supply and demand through the Tableau visualization and data models.

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