Complete Data Science Roadmap For 2025 | Data Scientist Roadmap For Beginners 2025 | Simplilearn

Nov 22, 20240 likes102 views

In this Simplilearn video, we outline the key roadmap to stand out in the data science field by 2025! From mastering core programming languages like Python and using version control tools like Git to understanding essential data structures, algorithms, and SQL—this guide has it all. We dive into crucial math and stats areas such as calculus, linear algebra, and probability, as well as data preprocessing and visualization with Numpy, Pandas, Matplotlib, and Seaborn. Gain insights into machine learning fundamentals like supervised and unsupervised learning, plus key tools, deep learning concepts, and specializations in NLP, Computer Vision, and Big Data. This guide will give you all the skills to succeed as a top data scientist!

Complete Data Science Roadmap For 2025 | Data Scientist Roadmap For Beginners 2025 | Simplilearn

Data Scientist Roadmap
Version Control System
02
 git
commit
 git add
 git merge
 git status
 git config
 git push
 git pull
 git clone <path>
 git reset
2 weeks

Data Scientist Roadmap
Data Structures and Algorithms
03
• Arrays
• Stacks
• Queues
• Trees
• Searchin
g
• Sorting
• Optimizi
ng
3rd
Month – 4th
Month

Data Scientist Roadmap
SQL
04
• Create new
tables
• Alter
• Delete Unnecessary
records
• Run Queries to
filter
• Run Queries to
sort
• Run Queries to
aggregate
5th
Month

Data Scientist Roadmap
Mathematics and Statistics
05 6th
Month – 7th
Month

Data Scientist Roadmap
Data Preprocessing and Visualization
06 9th
Month – 10th
Month

Data Scientist Roadmap
Machine Learning Fundamentals
07
Supervised Learning. Unsupervised Learning.
11th
Month – 13th
Month

Data Scientist Roadmap
Deep Learning
08
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
13th
Month – 14th
Month

Data Scientist Roadmap
Specializations
09
Computer Vision
Natural Language Processing (NLP)
16th
Month – 17th
Month

Data Scientist Roadmap
Big Data
10 18th
Month – 19th
Month

1) Data analytics involves treating available digital data as a "gold mine" to obtain tangible outputs that can improve business efficiency when applied. Machine learning uses algorithms to correlate parameters in data and improve relationships. 2) The document provides an overview of getting started in data science, covering business objectives, statistical analysis, programming tools like R and Python, and problem-solving approaches like supervised and unsupervised learning. 3) It describes the iterative "rule of seven" process for data science projects, including collecting/preparing data, exploring/analyzing it, transforming features, applying models, evaluating performance, and visualizing results.

00-01 DSnDA.pdfSugumarSarDurai

The document discusses the growth of data and the field of data science. It begins by noting the large amounts of data being generated daily by various sources like web/e-commerce transactions, social networks, and scientific projects. It then discusses some of the challenges of big data including volume, velocity, and variety. The document provides an overview of the multidisciplinary nature of data science and the skills required of data scientists. It also summarizes different approaches to and job roles in data science.

Building Data ScientistsMitch Sanders

This is ChatGPT Book Data Science Roadmap.pdfimransahito82

Python tool to data analysis and artificial intelligenceMd Aksam VK

This document provides an overview of Python tools for data analysis and artificial intelligence. It discusses various data types and structures in Python like lists, tuples, and dictionaries. It also covers data exploration techniques including reading files, handling different variable types, and data visualization. Machine learning algorithms like label encoding, logistic regression, naive bayes classification, k-means clustering, and support vector machines are explained. Code files with examples of implementing these techniques in Python are referenced.

Semantics-enhanced Geoscience Interoperability, Analytics, and ApplicationsArtificial Intelligence Institute at UofSC

Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...Spark Summit

Elasticsearch provides native integration with Apache Spark through ES-Hadoop. However, especially during development, it is at best cumbersome to have Elasticsearch running in a separate machine/instance. Leveraging Spark Cluster with Elasticsearch Inside it is possible to run an embedded instance of Elasticsearch in the driver node of a Spark Cluster. This opens up new opportunities to develop cutting-edge applications. One such application is Dataset Search. Oscar will give a demo of a Dataset Search Engine built on Spark Cluster with Elasticsearch Inside. Motivation is that once Elasticsearch is running on Spark it becomes possible and interesting to have the Elasticsearch in-memory instance join an (existing) Elasticsearch cluster. And this in turn enables indexing of Datasets that are processed as part of Data Pipelines running on Spark. Dataset Search and Data Management are R&D topics that should be of interest to Spark Summit East attendees who are looking for a way to organize their Data Lake and make it searchable.

2. Data Preprocessing.pdfJyoti Yadav

The document outlines machine learning practicals using Python. It includes 14 practical programming assignments on topics like scatter plots, linear regression, decision trees, k-nearest neighbors, and clustering. It also provides an overview of Python libraries for machine learning like NumPy, Pandas, Scikit-Learn, and Matplotlib for tasks like data preprocessing, modeling, visualization, and more. Data preprocessing concepts covered are importing data, handling missing values, encoding categorical variables, and splitting data into training and test sets.

Data Science At ZillowNicholas McClure

This document provides an overview of data science work at Zillow. It discusses Zillow's use of machine learning models like the Zestimate and Rent Zestimate to analyze housing data. It describes Zillow's technology stack, which heavily leverages Python, R, and SQL. Specific examples are provided on automated waterfront determination using GIS data and discovering home street features. The document also discusses how tools like Dato and Scikit-Learn are used for tasks like fraud detection, property matching, and data modeling. In closing, current job openings at Zillow are listed.

Data Science & Big Data - Theory.pdfRAKESHG79

This document outlines the curriculum for the course "Elective Theory II - Data Science and Big Data" for the VI semester of the Diploma in Computer Engineering program. The course covers 5 units over 80 hours on data science fundamentals, data modeling, and big data concepts including storage and processing. The objectives are to understand data science techniques, apply data analysis in Python and Excel, learn about big data characteristics and technologies like Hadoop, and explore applications of big data. Topics include linear regression, classification models, MapReduce, and using big data in fields such as marketing, healthcare, and advertising.

Lecture1 introduction to big datahktripathy

This document provides a syllabus for a course on big data. The course introduces students to big data concepts like characteristics of data, structured and unstructured data sources, and big data platforms and tools. Students will learn data analysis using R software, big data technologies like Hadoop and MapReduce, mining techniques for frequent patterns and clustering, and analytical frameworks and visualization tools. The goal is for students to be able to identify domains suitable for big data analytics, perform data analysis in R, use Hadoop and MapReduce, apply big data to problems, and suggest ways to use big data to increase business outcomes.

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Unit 1karthik eriki

This document provides an introduction to data mining concepts and techniques. It discusses why data mining is necessary due to the explosive growth of data from various sources. Data mining involves the automated analysis of massive datasets to discover hidden patterns and knowledge. It is part of the broader knowledge discovery process that includes data cleaning, integration, selection, mining, and pattern evaluation. Data mining draws from multiple disciplines including database technology, statistics, machine learning, and pattern recognition to extract useful insights from large, complex datasets.

data miningnehaanand123

This document provides an overview and introduction to data mining. It defines data mining and distinguishes it from databases. The document outlines basic data mining tasks like classification, clustering, regression, and summarization. It also discusses the relationship between data mining and knowledge discovery in databases (KDD) and covers data mining models, development, issues, and metrics.

Part1Amit Sharma

Data Science Fundamentals and Practices.pptxjustjoking99yt

Federated Ontology Based Query System George Sam

This document describes an integrated ontology project for sports domains including cricket, football, and tennis. The project involves extracting data from web sources, cleaning the data, creating a federated ontology using existing ontologies, modeling the data in Karma, publishing the data as RDF triples in a triple store, and enabling querying of the data using SPARQL. Future work may include adding additional sports domains and developing interfaces for querying and applications.

Data_Science_Basic_Tutorial________.pptxhisyambasriid

romi-dm-aug2020.pptxAgusPerdanaWindarto1

Data mining is the process of extracting useful knowledge from large amounts of data. It involves converting data into information and discovering patterns that can be used to make predictions. Some key applications of data mining include predicting student graduation, legislative election results, creditworthiness, fraud detection, and forest fire risk. Data scientists play an important role in analyzing data, developing predictive models, and integrating knowledge into software systems to create intelligent applications that deliver value for businesses and organizations.

CLIM Program: Remote Sensing Workshop, Distributed Access and Analysis: NASA ...The Statistical and Applied Mathematical Sciences Institute

Data systems in NASA?s Earth Science Division are primarily focused on providing stewardship of the products of remote sensing and are manifested as Digital Active Archive Systems. Each Instrument Team has a related Science Team which defines the algorithms and monitors the processing of the output of the instruments to produce the related data products and in an format and standards compliance of them. These teams are influenced also by the research and applied sciences components of the programs, but the primary focus is on proving the ongoing validity of the products. Across the distributed system, every product is different. However, this is not conducive to analytics. NASA?s Advanced Information Systems Technology (AIST) program is developing an entirely new approach to creating Analytic Centers which focus on the scientific investigation and harmonize the data, computing resources and tools to enable and to accelerate scientific discovery. Stay tuned to _nd out how. A major element, in today’s science interests, is the comparison of multi-dimensional datasets; this warrants considerable experimentation in trying to understand how to do so meaningfully and quantitatively; asked another way, \What do you mean by similar?" Uncertainty quantification has evolved considerably in the arenas of data reduction and full physics models; however, the emerging demand for machine learning and other artificial intelligence techniques has failed to keep uncertainty quantification and error propagation in mind and there is considerable work to be done.

Bike Sharing Demand: Akshay PatilAkshay Patil

This document presents a research project on predicting bike sharing demand using machine learning models. The primary objective is to build a statistical model to predict bicycle rentals using available data. Secondary objectives are to learn how real-time data is represented in datasets, understand data pre-processing, and compare results of regression, decision trees, random forests and SVM models. The proposed methodology includes fetching data, cleaning missing data, feature engineering, and building/validating predictive models. The document describes analyzing bike sharing training and test data, creating new features, and implementing models in R and Weka.

Spark at NASA/JPL-(Chris Mattmann, NASA/JPL)Spark Summit

The document discusses NASA's use of Apache Spark for big data analytics. It provides context on Chris Mattmann's involvement with Spark through his roles at NASA JPL and the Apache Software Foundation. It outlines some of NASA's big data challenges around handling large volumes of Earth observation data from instruments and simulations. NASA is interested in using Spark for tasks like data triage, archiving, and knowledge extraction to help address these challenges and enable new scientific insights.

Spark Summit EU 2015: Combining the Strengths of MLlib, scikit-learn, and RDatabricks

This talk discusses integrating common data science tools like Python pandas, scikit-learn, and R with MLlib, Spark’s distributed Machine Learning (ML) library. Integration is simple; migration to distributed ML can be done lazily; and scaling to big data can significantly improve accuracy. We demonstrate integration with a simple data science workflow. Data scientists often encounter scaling bottlenecks with single-machine ML tools. Yet the overhead in migrating to a distributed workflow can seem daunting. In this talk, we demonstrate such a migration, taking advantage of Spark and MLlib’s integration with common ML libraries. We begin with a small dataset which runs on a single machine. Increasing the size, we hit bottlenecks in various parts of the workflow: hyperparameter tuning, then ETL, and eventually the core learning algorithm. As we hit each bottleneck, we parallelize that part of the workflow using Spark and MLlib. As we increase the dataset and model size, we can see significant gains in accuracy. We end with results demonstrating the impressive scalability of MLlib algorithms. With accuracy comparable to traditional ML libraries, combined with state-of-the-art distributed scalability, MLlib is a valuable new tool for the modern data scientist.

Big data – An Introduction, July 2013Peter Morgan

This document provides an overview of big data, including its definition, sources, databases, and analytics. It defines big data as large datasets greater than terabytes in size that are increasingly being collected from various sources such as science, social media, government and more. It notes that most data is unstructured. It also discusses the evolution of databases from relational SQL databases to non-relational NoSQL databases and Hadoop. Finally, it outlines the major tools and technologies used for big data analytics, including MapReduce, Hadoop, and machine learning.

WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov

In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive. http://clds.sdsc.edu/wbdb2015.ca/program

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