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Data science is an interdisciplinary field that involves the use of statistical and computational methods to extract insights and knowledge from data. It combines techniques and tools from statistics, mathematics, computer science, and domain-specific fields to analyze and interpret complex data sets.

Data science has become a popular career choice for several reasons:

  • High demand: Data science is a rapidly growing field with a high demand for skilled professionals. As companies increasingly rely on data to drive decision-making, the demand for data scientists has increased across various industries.
  • High earning potential: Data scientists typically earn a high salary, reflecting the value of their specialized skills and the demand for their expertise. According to various salary surveys, data scientists can earn a six-figure salary in many industries.
  • Diverse career opportunities: Data science offers diverse career opportunities, ranging from entry-level data analyst roles to senior data scientist positions. Data scientists can work in various industries, including tech, healthcare, finance, marketing, and social sciences.
  • Intellectual challenge: Data science involves working with complex data sets, applying statistical methods and machine learning algorithms, and developing innovative solutions to real-world problems. For those who enjoy intellectual challenge, data science can be a rewarding career.
  • Flexibility:Many data science roles offer flexible working arrangements, including remote work and flexible schedules. This can be particularly appealing to those who value work-life balance.

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    CURRICULUM

    Data Science in Real Life

    • Defining Data Science
    • What Does a Data Science Professional Do?
    • Data Science in Business
    • Use Cases for Data Science
    • Data Science People

    Basic Analytics

    • Data Types
    • Basic statistics using data examples
    • Central tendencies
    • Correlation analysis
    • Data Summarization
    • Data Dictionary
    • Outliers /Missing Values
    • Basic Linear Algebra – dot product, matrix multiplication and transformations

    Data Wrangling

    • Data cleaning
    • Data transformation
    • Data merging
    • Data reshaping
    • Data aggregation
    • Data validation
    • Data imputation
    • Data standardization
    • Data filtering

    Introduction to R

    • History and evolution of R
    • Principle and software paradigm
    • Description of R interface
    • Advantages of R
    • Drawbacks of R
    • So why use R?
    • References for learning

    Advance Data Manipulation in R (Packages like DPLYR, PLYR, SQLDF, MASS)

    • Importing and exporting data from .txt files and .xls-like files
    • Advanced data manipulation
    • Accessing variables and management of subsets in data
    • Working with characters, text and dates

    Exploratory Analysis & Data Visualization

    • Graphics for basic descriptive statistics
    • Graphics for time-related data
    • Introduction to more advanced graphics
    • Adding relevant information & customize your graphics

    Fundamental of Statistics

    • Types of Variables, measures of central tendency and dispersion
    • Variable Distributions and Probability Distributions
    • Normal Distribution and Properties
    • Central Limit Theorem and Application

    Basic Statistical Analysis

    • Statistics Basics Introduction to Data Analytics, descriptive and summary
    • Inferential statistics

    Statistical Significant Tests

    • Hypothesis Testing Null/Alternative Hypothesis formulation
    • Z‐Test, T‐Test, Chi‐Square test
    • Analysis of Variance (ANOVA)
    • Chi Square Test
    • Correlation

    Data Preparation

    • Need for data preparation
    • Outlier treatment
    • Missing values treatment
    • Multicollinearity

    Predictive modeling & Time Series Analysis

    • Basics of regression analysis
    • Linear regression
    • Logistic regression
    • Interpretation of results
    • Multivariate Regression modeling

    Machine Learning Algorithm

    • Text Analytics
    • Random Forest
    • Support Vector Machine (SVM)
    • Naïve Bayes Algorithm
    • K-NN Classification & Regression

    Model Optimization

    • Overfit vs Underfit
    • Bias Variance tradeoff
    • Grid Search
    • Random Search
    • Feature Engg examples
    • Ridge / Lasso Regression
    • SkLearn Pipelines
    • SkLearn Imputers

    Big Data Hadoop and Spark Developer

    • Introduction to Big Data and Hadoop Ecosystem
    • HDFS and Hadoop Architecture
    • MapReduce and Sqoop
    • Basics of Impala and Hive
    • Working with Hive and Impala
    • Type of Data Formats
    • Advanced HIVE concept and Data File Partitioning
    • Apache Flume and HBase
    • Apache Pig
    • Basics of Apache Spark
    • RDDs in Spark
    • Implementation of Spark Applications
    • Spark Parallel Processing
    • Spark RDD Optimization Techniques
    • Spark Algorithm
    • Spark SQL

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