AI-Data Analyst - Rooman Technologies

AI-Data Analyst

AI-Data Analyst - A fast track to the success.
100% Placement Assistance | 1000+ Hiring Partners
4.5/5
6734 Learners Enrolled

Program Duration : 320 Hrs

At 15 - 20 hrs/week

Classroom Based

Learning format

Branches Across India

Select your Preferred Branch

About Program

The role of Data Analyst has become very critical in today’s world with huge data acquired by companies. Data Analyst are expected to inspect, clean, transform and model various kind of data from Databases to Cloud to build various analysis using Statistical Analysis, Data Visualization and Machine Learning Algorithms. They specialize in understanding the business requirement, work with data and build compelling stories which would help business executives to make better decision. They assist Data Scientist in their complex model development work by performing exploratory data analysis.

Audience

  • B.E./ B.Tech. – Computer Science, IT, Electronics or equivalent
  • MCA, MBA or Masters in Quantitative Subject
  • Working Professional in IT aspiring to start a career in Data Science

Key Benefits

  • Data Architecture
  • Data Management
  • Data Quality
  • Statistics – Basic & Advanced
  • Machine Learning
  • Time Series
  • Text Analytics
  • Data Visualization

Course Curriculum

  • Mind set to success in data science
  • Industry trends over Data science
  • Essential Skills to success in Data Science
  • Different roles in Data Science
  • Different between Data Science, Data Engineering, Data Analytics, Machine Learning
  • Data Science Process
  • Data Science Portfolios
  • Story around Data science usage case
  • Converting your experience into Data Science field: One Use Case – To Build a story

  • Setting up free Jupyter notebook on Google
  • How to use Jupyter notebook
  • Variables in Python
  • Python Integer Data Type
  • String Data Type
  • Taking Input
  • Python Boolean Data Type
  • Python Blocks
  • if else statement
  • if elif else
  • Boolean Logic
  • While Loop
  • Python Lists
  • Python List Operations – Append, Index, Max. Min
  • Python Range
  • Python Functions
  • Passing variable arguments to functions
  • Python Modules
  • Python Exceptions
  • Python File Handling
  • None Data Type
  • Python Dictionaries
  • Tuples
  • List Slices
  • List Comprehensions
  • Python String Functions
  • Python List Functons – Any

Python List All – Function

  • Numpy – Add, Subtract, Multiply
  • Numpy Dot Product
  • Numpy Slicing
  • Mixing Integer Indexing And Slice Indexing
  • Numpy Array Indexing
  • More Array Indexing
  • Boolean Array Indexing
  • Numpy Sum
  • Numpy Reshape
  • More Numpy Reshape
  • Numpy Tensors 1D, 2D,3D
  • Numpy Transposing
  • Numpy Broadcasting
  • Pandas
  • Pandas Series
  • Pandas Series Index
  • Pandas Advantage Over Numpy
  • Pandas Loc and iLoc
  • Pandas example – Finding Max
  • Pandas Series Addition
  • Pandas Apply Function
  • Pandas Dataframes
  • Pandas DataFrames Introduction
  • Pandas DataFrame Index, Loc and ILoc
  • Pandas Sum Along Axis
  • Pandas DataFrame Addition
  • Pandas DataFrame ApplyMap
  • Pandas Reading A CSV File
  • Database Fundamentals
  • SQL Queries – Basic
  • SQL Queries – Advanced
  • Real World Examples of SQL based Analytics
  • MYSQL
  • Concept of Data Quality
  • Metadata Management
  • Data Lineage Concept & Application
  • Master Data Management
  • Data Stewardship as a new role
  • Permutations and combinations
  • Bayes Theron
  • Central Limit Theorem
  • Cumulative distribution function (CDF)
  • Probability Density Function (PDF)
  • Expected value of Discrete Random variable
  • Properties of Means and Variance
  • Binomial Probabilities
  • Mean, Variance, SD of Binomial Distribution
  • Negative Binomial Distribution
  • Geometric Distribution
  • Poisson Distribution
  • Uniform Distribution
  • Exponential Distribution
  • Normal Distribution
  • Population mean with known and unknown population standard deviations
  • Determining sample size
  • Confidence intervals for population proportions
  • Quantify minimum sample sizes to achieve certain margin of error in predictions
  • Z – Test
  • t – Test
  • Chi-square Test
  • F-Test
  • ANOVA
  • Type-1 and Type-2 error
  • Interpretation of Confidence level
  • Significance level and power of test
  • Computation and interpretation of P-value
  • Determine the sample size and significance level for a given hypothesis test
  • Data Types
  • Notation and Definitions
  • What is Population, Sample, and census?
  • What is different scale of measurements?
  • What is Simple Random Sample?
  • Measures of Central Tendency
  • Measures of Variability
  • Histogram: Relative Frequency, Frequency Distribution and Cumulative Distribution.
  • Skewness and Kurtosis.
  • Relations between Mean and SD
  • Box Plot
  • Bar chart
  • Pie Chart
  • Time plot
  • Scatter plot
  • Regression Models – Linear 
  • Regression Model – Logistics
  • Decision Tree Model
  • Unsupervised Machine Learning
  • Time Series Analysis
  • Text Analytics – Basics
  • Basic Data Visualizations – Bar Chart, Line Chart, Histogram
  • Intermediate Data Visualizations – Tree Map & Geospatial Analysis
  • Basic Dashboard Development in Tableau
  • Excel Data Import

Tools & Softwares

Need Guidance?

Talk to our experts on
079 4822 8880

Branches Across India

Our Alumni Work at

Certification Partners