Introduction to Data Science:
Data science is a multidisciplinary field that uses statistics, mathematics, information science and computer science in order to understand and solve the complex problem analytically. Data science is a combination of many fields such as database management, data analytics, predictive modeling, machine learning, big data, coding, data visualization, reporting etc.
How to become a Data scientist:
Start studying with any of the tools and coding like R/SAS/python/Hadoop. Know basic algorithm, statistics and mathematics.
Do an internship:
The internship must be chosen based on the interest and career growth. Here you can build an experience as a student or a graduate. Internships help you to work and gain knowledge in live projects.
SQL is a very important and basic tool. As an effective data scientist, you must know how to extract the data from the database using SQL. SQL is used in various data management task, querying a database, inserting and existing records. It will be a big advantage that, being a data scientist with SQL programmer.
Master in Success Metrics and tracking metrics:
Success metrics related to the active user’s and also it helps to figure out things which are worked in the past and to improve in the present. Tracking metrics which tracks the time spent by the user, video watching, force quits video upload.
For being a data scientist, Python is the main language to learn because of its rich ecosystem. Python is the very powerful language which is used for many application. Python emphasizes productivity and readability. To visualize the data using Tableau will improve your data awareness skill as a result that provides you a better future.
Big data knowledge:
Knowledge of bid data is required to analyze distributed computing big data and unstructured data analytics. Big data software (distributed computing tools) used for data science are Knime, OpenRefine, R-Programming, Hadoop, Talend, RapidMiner, Hive, Pig, Mahout, spark, java to complete delivery process.
A data scientist should be a master in database management, data blending, querying, data manipulation, ETL. Database tools like SQL/Mysql, OLAP cubes Teradata DB2, SQL Server, Oracle, Informix, Exadata are used for data administration tasks.
Analytical skills :
Analytical skills is the heart of a data scientist’s skill set. In-depth knowledge of at least one of these analytical tools( SAS/RSPSS/Python/weka/MATLAB) for data science. Generally, R is preferred. These tools are considered as conventional tools for data analytics.
Love to learn data science where ever you are:
For being a data scientist, look for knowledge everywhere because the data scientist must be master in analytical skills, be a keen viewer and love to learn data science.
Communication plays a very important role. Because conveying or explaining any problem and ability to share data analytics to people is an important job for a data scientist, here it will differentiate a good data scientist from a data scientist.
Presentation or visualization :
After collecting data the next exclusive step is to how we extract that data in an attractive and meaningful manner. The tools that can be used for presentation are excel, Tableau, QlikView, etc... Whatever the data we have, the destination is how we are presenting them.
Stay tuned with data science:
Stay tuned with websites like Reddit, Google News, kdnuggets, Kaggle, etc...to get updates regarding current scenarios, job opportunities etc. Update your skills day to day to get a better career in data science.
How to be a Data Scientist:
- Understanding the business problem.
Accumulating and understanding the required and available data
Manipulating the data and turning into a usable format
Designing the analysis, the metrics we are looking to extract
Determining the most optimal conclusion for the analyzing the data
Modeling the algorithm and evaluating the result
Being a data scientist you must face problems analytically and statistically, rather running out of them.
Data scientist Qualification:
- Diploma and Engineering students in an analytical concentration.
IT professionals and graduates who are looking for the career in data science and analytics.
A person who is interested in playing with data and have an aim for developing business acumen.
Data scientist skills:
Critical thinking, Data architecture, Coding, Machine learning, Communication, problem-solving, database knowledge, predictive analytics, big data knowledge, presentation skills.
Data science tools:
Matlab x 12 , Hadoop x 19 , R x 30, Python x 22,SAS x 18, Java x 15, Hive x 13, SQL x 27 .
Data scientist role:
SAS Data Analyst, IBM Data Analyst Data Scientist, Data Mining Engineer, Machine Learning Engineer, Big Data Scientist, Data Architect, Business Intelligence Architect, Enterprise Data Architect, Big Data Architect, Hadoop Engineer, Data Warehouse Architect, Senior Data Scientist, Senior Big Data Analyst.
Data Scientist Salary:
Average Data Scientist job salary per annum by experience – India
For 0-5 years experience - 5,675085 L
5-10 years experience – 6,817772 L
10-20 years experience – 7,646100 L
20 years or more experience – 9,493907 L
Demand for Data science:
This makes you clear about the demand for data science for the past 3 years in India.
So, in demand its right time to do the certification course in Data science. LIVEWIRE has given the opportunity to attend the online training in Data science. As we are ISO certified training institute our course completion certification is internationally recognized. Are you want to see your growth in your career and improve your skills, then this is the right time to signup.
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