As we all know the world is rapidly growing in the field of
Information Technology and we have to store lots of information
in the form of data, that a large amount of data is known as Big
Data.The challenge which comes with big data is storage and
access to the right information out of bulk data for business
analysis and predictions.
Fig: Structured, Unstructured, Semi-structured huge data
Before this era, the data was less and stored in a structured
manner; nowadays the data is huge and can be structured,
unstructured, and semi-structured.
Cleaning, Processing, Maintaining, Organizing
The solution which comes to store the big data is Hadoop and
other frameworks. Now the main attraction is cleaning,
processing, maintaining, organizing, and analyzing that data,
here the Data Science comes into play.
In short, Data Science is extracting meaningful data or
information from large and complex datasets.
Data Analysis, and Machine Learning
Data Analysis is the process of inspecting, cleaning,
transforming, and modeling data to extract meaningful insights
and support decision-making. It involves various steps,
including data collection, cleaning to remove errors, and
exploratory data analysis (EDA) to uncover patterns and
relationships. Data analysis is crucial in fields like
business, healthcare, and technology, helping organizations
make informed decisions based on empirical evidence.
Machine Learning is a subset of artificial intelligence
(AI) that focuses on developing algorithms that enable
computers to learn from data and make predictions or decisions
without being explicitly programmed. By using techniques like
supervised learning, unsupervised learning, and reinforcement
learning, machine learning models can identify patterns in
data and apply this knowledge to new, unseen data. Machine
learning is widely used in applications such as recommendation
systems, image recognition, and natural language processing.
Opportunities
Now let's discuss what the scopes and career opportunities are
available in data science, along with the required tools and
technologies:
1. Data Analyst
A Data Analyst performs mining of huge amounts of data, models
the data, looks for patterns, relationships, trends, and so
on. Then they visualize and report for analyzing the data for
decision making and problem-solving processes.
Skills Required:
- Mathematics
- Business Intelligence
- Data Mining
- Basic knowledge of Statistics
Technologies and Tools:
- MATLAB
- Python
- SQL, Hive, Pig
- Excel, SAS, R, JS, Spark, etc.
2. Data Engineer
A Data Engineer works with a massive amount of data and is
responsible for building and maintaining the data architecture
of a data science project. Data engineers also work for the
creation of dataset processes used in modeling, mining,
acquisition, and verification.
Tools and Technologies:
- SQL, MongoDB, Cassandra, HBase
- Apache Spark, Hive, MapReduce
- Python, C/C++, Java, Perl, etc.
3. Machine Learning Expert
A Machine Learning expert works with various machine learning
algorithms used in data science, such as regression,
clustering, classification, decision tree, random forest, etc.
Skills Required:
- Understanding of various algorithms
- Problem-solving analytical skills
- Probability and statistics
Technologies and Tools:
- Python, C++, R, Java
- Hadoop
4. Data Scientist
A Data Scientist works with an enormous amount of data to come
up with compelling business insights through the deployment of
various tools, techniques, methodologies, algorithms, etc.
Skills Required:
- Statistics
- Mathematics
- Visualization and communication skills
Tools and Technologies:
- R, SAS, SQL, Python
- Hive, Pig, Apache Spark, MATLAB
5. Data Architect
Data architects build complex computer database systems for
companies, either for the general public or for individual
companies. They work with a team that looks at the needs of
the database, the data that is available, and creates a
blueprint for creating, testing, and maintaining that
database.
