Data Science & AI Newsletter

Welcome all of you in Data Science & AI newsletter from DataThick

In this newsletter you will get update related to below tools and & technologies.

Artificial Intelligence - AI | Big Data Business Analytics | Business Intelligence - BI | Data Science |?Data Visualization | GIS | Machine Learning | Deep Learning | Statistics | Cryptocurrency | Data Analysis | Data Engineering | Data Governance | Data Modeling | Data Privacy | Python or R | Java | C++ | TensorFlow | PyTorch | NLP - Natural Language Processing | Database| SQL | Tableau | Power BI | QlikView | AI Engineer | Data Scientist | Computer Science | Spark | Scala | GoLang | MapReduce | Hive | Pig | Spark | Hadoop | Mathematical and algorithms | Image processing | Computer Vision | Neural Network | Predictive Analytics | Intelligent Bots | AWS | Cloudera |Internet of Things- IoT | Keras | MATLAB | Data Scientist | Data Analyst | Machine Learning Engineer | Big Data Engineer / Big Data Architect | Research Scientist | Business Intelligence Developer

??Below are the some common Skills for data scientists & Analyst?which will be cover as well in this page.

·?????R Programming

·?????Python

·?????Hadoop

·?????SQL

·?????Algebra, Statistics, and ML

·?????Data visualization tools

·?????Business acumen?

·?????Microsoft Excel

Data engineer skills

·?????SQL?

·?????Database systems?- Data engineers need to be fluent in SQL-based systems like MySQL, PostgreSQL Microsoft SQL Server, and Oracle Database as well as to be comfortable with NoSQL databases, including MongoDB, Cassandra, Couchbase, Oracle NoSQL Database.

?·???????ETL solutions - Data engineers need to have ETL tools in their toolkit to build processes to move data between systems. Examples of such technologies can be SAP Data Services, StitchData, Xplenty, Informatica, and Segment.

?·???????Data warehouse software - The ability to set up a cloud-based data warehouse and connecting data to it are essential to this role. Some of the data warehousing solutions include Amazon Redshift, Panoply, BigQuery and Snowflake.?

?Big Data Tools - The most popular ones are Apache Spark, Apache Kafka, Apache Hadoop, Apache Cassandra, the first two being a common requirement. As such, it makes sense to concentrate on gaining a strong understanding of them. Knowledge of Hadoop-based technologies is a frequent requirement for this position as well.?

?Required skills for a Full Stack Data Scientist

• An in-depth knowledge of algorithms, statistics, mathematics and machine learning.
• Programming languages like R, Python, SQL, SAS, and Hive.
• Business understanding and the aptitude to point out the right questions to ask, and find answers in the available data.
• Strong communication skills in order to communicate the results effectively to the rest of the team.
• ?Data Science Generalist
• ?Deep Learning
• ?Natural Language Processing
• ?Business Intelligence / Data Analytics
• ?Business Analytics
• ?Data Engineering
• ?Data Science Generalist
• ?Deep Learning
• Data Science for Business Analytics and Intelligence

·????????Knowledge of basic Statistical Language such as Python, R, Julia

·????????Good understanding of Data Science Library including Numpy, Pandas, Scipy, Seaborn

·????????ML/DL Library Experience: Tensorflow, Torch, scikit-learn

·????????Unstructured Data Processing: Image, Text, Sounds

·????????Relational Database: MySQL, PostgreSQL, SQLite

·????????Distributed File System: Hadoop, Spark, AWS, MongoDB

·????????Container-type virtual environment: Docker

·????????Version control system: GitHub

·????????Web Framework: Django, Flask, Ruby on Rails

·????????Data collection

·????????Data processing, cleaning, and transformation

·????????Features engineering

·????????Programming and writing maintenable, production ready data science scripts/applications

·????????Statistical analysis

·????????Machine learning

·????????Data visualisation

·????????Reporting and good communication

Basic stages in the data science lifecycle that can be owned by a full stack data scientist:

1.????Business problem.?Unless research-oriented, all data science projects should start with a problem that adds value to a business either through efficiency gains, automation, or new capabilities.

2.??Data collection/identification. Machine learning requires quality data to build a quality model for use.

3.???Data exploration and analysis. The data must be analyzed and understood before a model can be built.

4.??Machine learning. Train a model to solve the business problem given the data.

5.???Model analysis and acceptance. Analyze the model results and behavior. Share with stakeholders for approval.

6.??Model deployment. Make the model accessible to the end-user.

7.???Model monitoring. Ensure that the model behaves as expected in the future.

?Data Analysts Skills -

·???????Data Mining

·???????Data Warehousing

·???????Math, Statistics

·???????Tableau and Data Visualization

·???????SQL

·???????Business Intelligence

·???????SAS

·???????Advanced Excel skills

?Chief Data Scientist Skills -

·???????Data Mining

·???????Data Warehousing

·???????Math, Statistics, Computer Science

·???????Tableau and Data Visualization/Storytelling

·???????Python, R, JAVA, Scala, SQL, Matlab, Pig

·???????Economics

·???????Big Data/Hadoop

·???????Machine Learning

·???????Ability to leverage machine learning and artificial intelligence (AI)

·???????Ability to apply math and statistics appropriately

·???????Ability to leverage self-service analytics platforms

·???????Ability to prepare data for effective analysis

·???????Analytics: The Head of Data Science will also play an analytical role where he will drive experimental data modeling designs within the business.

·???????Collaboration: The role of the Head of Data Science is a highly collaborative one and in this position he works closely with other Data and Analytics Teams, inclusive of data analytics, data warehousing, and data engineering teams in creating big data applications

·???????Strategy: The Head of Data Science also plays a strategic role where he is tasked with continuously improving the business’s data analysis model, creating industry-leading performance.?The Head of Data Science also scopes, designs, and implements machine-learning models to support the business’s numerous initiatives and programs.

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