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Data Science Course in Andheri
Data Science Course in Andheri What is data science? Become a data scientist after 12th, there will be tremendous growth in career
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Best Data Science Course in Andheri
In this age of growing information technology, the importance of data has increased. Data science is one such field in which the youth have the opportunity to get a job and make a great career.
You often come across the word Data Science. People have a lot of questions about it in their minds. Often people are seen trying to solve these questions here and there. In this copy, we are not only going to tell the courses of Data Science for 12th pass students but are also trying to answer all the questions related to it. First of all, it is necessary to know that the word Data Science has no direct meaning with science. It is the new king of the internet world.
As the importance of data is increasing in the country and the world, job opportunities like data scientist, data analytics are also coming at the same pace. It is even being said that there will be a lot of scope for courses related to this in the coming 10-15 years. Because there is going to be a lot of jobs here. If you are fond of playing with computers, laptops, and cannot sleep without internet, then any course related to data science is for you.
In this age of growing information technology, the importance of data has increased. Data science has emerged as an emerging field. The race to get admission and study in courses related to it has also increased. Which courses can be taken for studying data science after 12th and what are the career prospects in it? Students have many options in this.
What is Data Science?
In simple terms, data science is the study of data, which involves algorithms, principles of machine learning and various other tools used to record, collect and analyze data to extract important and useful information. Data scientists extract and examine data from a wide range of sources such as log files, social media, sensors, customer transactions, etc.
Who are data scientists?
The person who analyzes data is called a data scientist. The person who analyzes and calculates the impact of data requires professional expertise. The software engineer needs skills such as linear algebra, machine learning, programming in statistics, and data visualization.
Why become a data scientist?
Read the points below to know why to become a data scientist-
- Data scientists in India can earn INR 08-23 lakhs per year, which is much higher than any other profession like engineering.
- Students from computer science, engineering, statistics, or mathematics background can choose to pursue data science courses, as it is a better-paying alternative to B Tech or mainstream engineering courses.
- Even students from management background can opt for data science instead of MBA.
- An IBM report predicts over 59% demand for data scientists in the finance and insurance sector, thus with the right skills, even finance and management students can experience a career boost.
- Indeed, a popular job listing website records a 29% increase in data scientist job postings every year, which means data science is one of the in-demand professions.
Skills to become a data scientist
Being an interdisciplinary field, data science requires not just one or two but a diverse set of technical skills and knowledge across the field of computer science. Here are the following skills that a data scientist must possess, regardless of the experience gained-
- Python Coding: Python is the most important coding language in data science as it helps in developing models for data mining, machine learning or web scraping. Taking various formats of data, Python can help you create and search datasets and import SQL tables into your code.
- R Programming: R is a program that is usually built for data analysis and provides formulas and methods for information processing and statistical analysis.
- Machine Learning and AI: Learning various techniques of machine learning such as logistic regression, decision tree, supervised machine learning, time series, computer vision, outlier detection, survival analysis, natural language processing, etc. is naturally important to tackle many challenges in the field.
- Hadoop Platform: Whenever the amount of data is too high, it can exceed the memory of the system and the Hadoop platform helps the data scientist to send or transfer the remaining data to various servers. This platform is also useful for data filtration, data sampling and summarization, exploration, etc.
- SQL: Structured Query Language (SQL) is a programming language that helps you communicate, access, and manage databases by adding, subtracting, or extracting data. Data scientists need to be skilled in SQL, as it is specifically designed to save time and reduce the amount of programming for difficult queries, through its compact commands.
Other Skills
- Data Visualization
- Business Acumen
- Communication Skills
- Data Wrangler
- Algebra and Calculus
- Statistics
- Java
- Unix
- PHP
Jobs and Responsibilities of a Data Scientist
After completing the education and mastering the required technicalities in data science, here are some of the most common roles and responsibilities you will perform as a data scientist-
- Staying up-to-date with all the emerging tools and techniques in statistical modeling, machine learning, etc.
- Mine the data and generate a hypothesis to help achieve high-level business objectives.
- Analyze large data sets using the Hadoop platform.
- Work in collaboration with IT managers, statisticians, programmers, and other experts to make business decisions or create products and services.
- To solve various analytical problems with incomplete datasets, data scientists also develop specially designed algorithms.
Career Scope
After this you can do a degree or diploma course in data science. Studying data science brings a plethora of career opportunities in various fields. It is not limited to being a data scientist, you can opt for various other job profiles as a data scientist under this vast domain-
Data Analyst
A data analyst is responsible for transforming a dataset into a useful structure, such as representations, reports, graphs, etc. They are the ones who collect, refine, display, and analyze statistical data to support and impact the objectives of a business. Being an entry-level position in the organizational chart of a business, a data analyst must have a deep knowledge of Python, R, C, C++, HTML, SQL, Machine Learning, Excel, Probability, and Statistics. They work closely with various departments and experts in the business to identify key business risks and performance in the compliance of data and convert them into a simple and legible format.
Business Analyst
Although a business analyst is technically less skilled than their other counterparts in data science, they still have a strong knowledge of all commercial procedures and have a solid business intelligence knowledge. Acting as a nexus between IT and business administration, a business analyst is responsible for processing basic data through various data visualization tools and data modeling.
They mostly focus on preparing the data in the form of graphs, charts, reports, etc., which can be easily read and ultimately serves the interest of the business. If you are planning to work as a business analyst, you will need a strong educational background in computer science, statistics, mathematics, business administration, economics, finance, or other related fields.
Data Engineer
Skilled in coding languages like Python, SQL, R, Java, Ruby, MATLAB, Hive, Pig, SAS, etc., data engineers design, produce, and manage large chunks of information or data. This is one of the most exceptional careers in data science, as a data engineer focuses on engineering the hardware that facilitates the data activities of a business.
Data engineers are responsible for developing an architecture that helps process and analyze data in a way that is best suited for a business organization. After obtaining an advanced degree and significant years of experience in the field of data science, one can secure a senior position under this career profile.
Apart from these three main and most sought-after career perspectives in data science, here are some other common job profiles you can consider:
Marketing analyst
Data and analytics manager
Statistician
Machine learning engineer
Database administrator
Data mining specialist
Best Data Science Course in Andheri Course Syllabus
● Data Types
● Measure Of central tendency
● Measures of Dispersion
● Graphical Techniques
● Skewness & Kurtosis
● Box Plot.
● Random Variable
● Probability
● Probability Distribution
● Normal Distribution
● SND
● Expected Value
● Sampling Funnel
● Sampling Variation
● Central Limit Theorem
● Confidence interval
● Introduction to Hypothesis Testing
● Hypothesis Testing ( 2 proportion test, 2 t sample t test)
● Anova and Chi Square
● Principles of Regression
● Intro to Simple Linear Regression
● Multiple Linear Regression
● Logistic Regression
● Data Cleaning
● Imputation Techniques
● Data analysis and Visualization
● Scatter Diagram
● Correlation Analysis
● Transformations
● Encoding Methods - OHE, Label Encoders,Outlier detection-Isolation Forest and Calculating the Predictive Power Score (PPS)
● Clustering introduction
● Hierarchical clustering
● K Means
● DBSCAN
● PCA
● Association Rules
● Recommender System
● Python Model Deployment
Regression Tasks / Classification Tasks
● Decision Tree
● KNN
● Support Vector Machines
● Feature Engineering (Tree based methods, RFE,PCA)
● Model Validation Methods (train-test,CV,Shuffle CV, and Accuracy methods)
● Lasso and Ridge Regressions
ANN
● Optimization Algorithm(Gradient descent)
● Stochastic gradient descent(intro)
● Back Propagation method
● Introduction to CNN
● Bagging and Random Forest
● Boosting
● XGBM
● LGBM
● Introduction to Text Mining
● VSM
● Intro to word embeddings
● Word clouds and Document Similarity using cosine similarity
● Named Entity Recognition
● Text classification using Naive Bayes
● Emotion Mining
● Introduction to Time Series
● Level
● Trend and Seasonality
● Strategy
● Scatter plot
● Lag plot
● ACF
● Principles of Visualization
● Naive forecasts
● Forecasting Error and it metrics
● Model Based Approaches
● AR Model for errors
● Data driven approaches
● MA
● Exponential Smoothing
● ARIMA
● Python Introduction- Programing Cycle of Python,PythonIDE and Jupyter Notebook
● Variables
● DataType
● Github
● HackerRank
● CodeWars and Sanfoundry Account Creation Number
● String
● List
● Tuple
● Dictionary
● Operator-Arithmetic
● Comparison
● Decision Making-Loops
● While Loop
● For Loop and Nested Loop
● Number Type Conversion-int(), long().Float()
● Strings-EscapeChar
● String Special Operator
● String Formatting Operator
● Python List
○ Accessing values in list
○ Delete list elements
○ Indexing, Slicing & Matrices
● Tuples
○ Accessing values in Tuples
○ Delete Tuples elements
○ Indexing
○ Slicing & Matrices
● Dictionary
○ Accessing Values from Dictionary
○ Deleting and Updating Elements in Dict
○ Properties of Dist
○ Built-In Dist Functions & Methods
○ Dict Comprehension
● Function
○ Define Function
○ Calling Function
○ Pass by Reference as Value
○ Function Arguments
○ Anonymous Functions
○ Return Statements
● Scope of Variables
○ Local & Global
○ Decorators and Recursion
○ Import Statements
● Locating Modules
○ Current Directory
○ Python path
○ Dir() Function
○ Global and Location Functions & Reload() Functions
○ Sys Module and Subprocess Module
○ Packages in Python
● Files in Python
○ Reading Keyboard Input
○ Input Function
○ Opening and Closing Files
○ Syntax and List of Modes
○ Files Object Attribute Open,Close.
○ Reading and Writing Files
○ File Position Directories Mkdir Method
○ Chdir() Method
○ Getcwd Method
○ Rmdir
● Exception Handling
○ List of Exceptions
○ TryandException
● OOP Concepts, Class, Objects, Inheritance, Overriding Methods like __init__, Overloading
Operators, Data Hiding
● Match Function
● Search Function
● Matching Vs Searching
● Regular Exp Modifiers and Patterns
● Database Connectivity
● Methods
○ MySQL
○ Oracle
○ How to Install MySQL
○ DB Connection
● Introduction to Databases
● Introduction to RDBMS
● Different types of RDBMS
● Software Installation(MySQL Workbench)
● What is Tableau ?
● What is Data Visualization ?
● Tableau Products
● Tableau Desktop Variations
● Tableau File Extensions
● Data Types
● Dimensions
● Measures
● Aggregation concept
● Tableau Desktop Installation
● Data Source Overview
● Live Vs Extract
● Bar Chart
● Pi-Chart
● Heat Maps
● Histogram
● Maps
● Scatterplot
● Donut Chart
● Waterfall Chart etc..
● Dual axis
● Blended axis
● Dimension Filter
● Measure Filter
● Data Source Filter
● Extract Filter
● Context Filter
● Quick Filter
● Basic Calculations
● Table Calculations
● Quick Table Calculations
● LOD's
● KPI's
● Joins
● Relationship
● Data Blending
● Union
● Hierarchy
● Group
● Sets
● Parameters
● Reference Lines
● Trend Line
● Forecasting
● Clustering
● Dashboard Objects
● Dashboard Actions
● Tableau Public website
● Data Definition language
● Data Manipulation Language
● Data Query Language
● Transactional Control Language
● Data Control Language
● SELECT
● LIMIT
● DISTINCT
● WHERE
● AND
● OR
● IN
● NOT IN
● BETWEEN
● EXIST
● ISNULL
● IS NOT NULL
● WILD CARDS
● ORDER BY
● GROUP BY
● HAVING
● COUNT
● SUM
● AVG
● MIN
● MAX
● COUNT
● String Functions
● Date & Time Function
● NOT NULL
● UNIQUE
● CHECK
● DEFAULT
● ENUM
● Primary key
● Foreign Key (Both at column level and table level)
● Inner
● Left
● Right
● Cross
● Self Joins
● Full outer join
● Index
● View
● Sub-query
● Window Functions
● Stored Procedures
● Exception Handling
● Loops
● Cursor
● Triggers
● Introduction
● DeepLearningImportance[Strength & Limitation]
● SP | MLP Neural Network Overview
● Neural Network Representation Activation Function
● Loss Function Importance of Non-LinearActivation Function
● Gradient Descent for NeuralNetwork
● Train
● Test & Validation Set
● Vanishing &ExplodingGradient
● Dropout Regularization
● OptimizationAlgo
● LearningRate
● Tuning
● Softmax
● CNN
● Deep Convolution Model
● Detection Algorithm
● CNN FaceRecognition
● RNN
● LSTM
● BiDirectionalLSTM
● Introduction to BigData, Challenges in Big Data and Workarounds| Introduction to Hadoop
and Its Components|HadoopComponents and Hands-On|Understand the Map Reduce
and Its Drawbacks
● Introduction to Spark and DataBricks|Spark Components, Spark MLlib Spark & DataBricks
and Hands-On One ML Model in Spark
● Cloud Computing
● Azure Cloud Platform
● Cloud Applications
● Cloud Services
● Open AI Studio
● Data Structures & Operators in R|Conditional Statement|Decision
Making|Loops|Strings|Functions|How to Import Data set in R| Programming Statistical
Graphics
● Introduction to ChatGPT and AI
● Types of AI and ChatGPT architecture
● ChatGPT Functionalities and Applications
● ChatGPT Prompt Engineering
Data Scientists Salary in India
Data scientists get very high salaries. The starting salary is INR 6-10 lakh per annum, other points are given below-
- As you gain experience as a data scientist, your salary will increase.
- In India, data scientists earn an average of INR 8.50 lakh per annum.
- Data scientists abroad get high salary packages.
Data Science Course in Andheri- Course Highlights
Experiential Learning
Advance Curriculum
100% Placement assistance
Classroom /virtual access
100% Practical training
Extended Time Access (ETA)
25 + Plus assignments
Live case study (Industry projects)
Industry Professionals as Faculty
Top Recruiting Companies
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FAQs
What are the eligibility criteria to pursue data science?
To pursue a degree in data science, it is essential to have a background in the relevant field and an understanding of the core concepts covered in the field.
What is the duration of data science courses?
The duration of data science courses can vary considerably depending on the level of qualification. Courses can be as short as 20 weeks for a diploma degree and can last up to several years if pursuing an established program such as a bachelors degree or masters in data science or a related field.
Is maths required for data science?
Knowledge of some basic concepts of maths such as algebra, calculus and statistics may be required for data science but having a background in maths is not mandatory.
Is coding required for data science?
It is important for a prospective student to have knowledge of programming languages such as C++, Java, Python as coding is an important aspect of data science.
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