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Data Science 360 - Data Science Certification Course

An extensive indusrty relevant Data Science course with global certification!

Data Science is one of the fastest-growing domains among digital technologies. Companies are ramping up to leverage data-driven decisions with a huge shift towards digital transformation. For this, they are always looking for skilled data scientists to join this new demanding workspace. Master data science skills by learning the theory, and practice with our Data Science 360, a global certification program. 


 


We bring to you a powerful pedagogy of world-class experts to help you learn and adapt to the ever-evolving world of data science. We combine learning tools alongside a hands-on approach to make you industry-ready. This data science course in India helps you furnish your knowledge from basics to advanced techniques. Learn the vital skills that will help you position yourself as a Data Scientist. Enroll in this Data Science Certificate course to develop comprehensive data science skills on data visualization descriptive analytics and predictive modeling along with Machine learning for driving smart business decisions.


 


Classroom and Bootcamp options are available for candidates looking for Data Science training in Bangalore, Noida, and Gurgaon. We are proud to share that this program is also rated among top certifications by prestigious publications like AIM & Higher Education review.


Data Science Course Duration: 400 hours (At least 140 hours live training + 50 hours e-learning + around 10 hrs of weekly self-study and practice)


CurriculumThis is one of the best Data Science certification for candidates who do not have a programming background but want to acquire job oriented practical skills on a prominent open-source Data Science platform. After completing this course, you will be able to apply robust data science solutions for your organization's data management needs that are key to business decision making.


 



  • The course curriculum starts by introducing you with Building Blocks of Data Science covering data science foundations, concepts, and basic programming elements. 

  • The next stage covers Data Visualization and Analytics (Excel, SQL & Tableau) elaborating on data extraction, manipulation, analysis, reporting, and building intuitive business dashboards. With interactive options, using business case studies 

  • R for Data science comes next to help you learn using R with databases, data import/export, analysis, and visualization.

  • Next, you will learn Python for Data science in two stages. The first one covers core Python, NumPy, and Pandas for data analysis. In the second stage, the course covers advanced data visualization using in-demand Python libraries.

  • After this, you move to learn the highly valuable skills of Predictive Modeling and Machine learning including supervised and unsupervised learning. 

  • In the next stage, you learn Text mining and how to use it for Natural Language Processing (NLP)

  • Next, you will be introduced to the concept of Deep Learning, and cloud computing for the overall understanding of the concept of AI & cloud computing. 


 


You complete your learning with an intensive Industrial and Functional session. This encompasses industrial sessions that give you an insight into project management, risk analysis, digital and social network analysis, and operational analysis. From a functional perspective, you get to learn about domains such as Banking & Financial Services, Retail and e-commerce, Pharma and healthcare, and Telecom and network.


Delivery Formats:



  • Data Scientist course in Delhi NCR (Gurgaon & Noida) and Bangalore is available in classroom and bootcamps batches as well. 

  • Fully interactive live online training (Global access)

  • Self-paced e-learning modules (Global access)

Who Should do this course?

This Data Science course is designed for beginners as well as candidates with an intermediate level of knowledge. Candidates with a background in engineering, finance, mathematics, statistics, business management, and computer programming are ideal for this course. However, if you are a non-programming person with an interest in Data Science, this course is for you as well.

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Course Duration 400 hrs
Classes 58
Tools Excel, VBA, SQL, Tableau, R, Python
Learning Mode Live/Video Based
Next Batch04th October, 2020 (Online)

Course Outline

  • Introduction to Bridge Course & Analytics Software’s Basic Excel
  • Basic Programming Elements
  • Introduction to Basic Statistics
  • RDBMS & SQL (Basics)
  • Introduction to Analytics & Data Science
  • Introduction to Mathematical Foundations

  • Quick Recap of Basics of Excel
    • Data manipulation using functions
    • Descriptive functions
    • Logical functions: IF, and, or, not 
    • Date and Time functions
    • Text functions
    • Array functions
    • Use and application of lookup functions
    • Limitations of lookup functions
    • Using Index, Match, Offset, reverse vlookup
  • Data analysis and reporting
    • Data Analysis using Pivot Tables - use of row and column shelf, values and filters
    • Difference between data layering and cross tabulation, summary reports, advantages and limitations
    • Change aggregation types and summarization
    • Creating groups and bins in pivot data
    • Concept of calculated fields, usage and limitations
    • Changing report layouts - Outline, compact and tabular forms
    • Show and hide grand totals and subtotals
    • Creating summary reports using pivot tables
  • Data Visualization in Excel
    • Overview of chart types – column/bar charts, line/area , pie, doughnut charts, scatter plots
    • How to select right chart for your data
    • Creating and customizing advance charts - thermometer charts, waterfall charts, population pyramids
  • Overview of Dashboards
    • What is dashboard & Excel dashboard
    • Adding icons and images to dashboards
    • Making dashboards dynamic
  • Create dashboards in Excel - Using Pivot controls
    • Concept of pivot cache and its use in creating interactive dashboards in excel
    • Pivot table design elements - concept of slicers and timelines
    • Designing sample dashboard using Pivot Controls
    • Design principles for including charts in dashboards - do's and dont's
  • Business Dashboard Creation
    • Management Dashboard for Sales & Services
    • Best practices - Tips and Tricks to enhance dashboard designing

  • Quick Recap of RDBMS & Basic SQL
  • Data based objects creation (DDL Commands)
    • Creating databases and tables. Understanding data types
    • Inserting values into the table
    • Altering table properties
    • Introduction to Keys and constraints
    • Creating, Modifying & Deleting Tables
    • Create Table & Create Index statements
    • Drop & Truncate statements – Uses & Differences
    • DDL Statements with constraints
    • Import and Export wizard to get the data in SQL server from excel files or delimited files
  • Data manipulation (DML Commands)
    • Data Manipulation statements
    • Insert, Update & Delete statements
    • Select statement – Sub setting, Filters, Sorting, Removing Duplicates, grouping and aggregations etc
    • Operators, predicates and built in functions(Top, distinct, Limit)
    • Where, Group By, Order by & Having clauses
    • SQL Functions – Number, Text, Date, etc
    • SQL Keywords – Top, Distinct, Null, etc
    • SQL Operators -  Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard operators and wildcard characters, etc
  • Accessing data from Multiple Tables using SELECT
    • Append and Joins
    • Union and Union All – Use & constraints
    • Intersect and Except statements
    • Table Joins - inner join, left join, right join, full join
    • Cross joins/cartisian products, self joins, natural joins etc
    • Inline views and sub-queries & it's types
    • Optimizing your work
    • Update operations with and without join
  • Advanced SQL
    • Creating table copy and database copy
    • Views
    • Transactions
    • Stored Procedures in SQL
    • Crud operations using stored procedures
    • Window functions in SQL
    • Miscellaneous Topics: Rollup and cube

  • Getting Started
    • What is Tableau?
    • Tableau product suite
    • How Does Tableau Work?
    • Tableau Architecture
    • Connecting to Data & data source concepts
    • Understanding the Tableau workspace
    • Dimensions and Measures
    • Data Types & Default Properties
    • Tour of Shelves & Marks Card
    • Using Show Me
    • Saving and Sharing your work-overview
  • Data handling & summaries
    • Date Aggregations and Date parts
    • Crosstab & Tabular charts
    • Totals & Subtotals
    • Bar Charts & Stacked Bars
    • Line Graphs with Date & Without Date
    • Tree maps/Scatter Plots
    • Individual Axes, Blended Axes, Dual Axes & Combination chart/Edit axis
    • Parts of Views
    • Sorting
    • Trend/Reference Lines/Forecasting
    • Filters/Context filters
  • Data handling & summaries
    • Sets (In/Out Sets/Combined Sets
    • Grouping/Bins/Histograms
    • Drilling up/down – drill through
    • Hierarchies
    • View data
    • Actions (across sheets)
  • Building Advanced Reports/ Maps
    • Explain latitude and longitude
    • Default location/Edit locations
    • Building geographical maps
    • Using Map layers
  • Calculated Fields
    • Aggregate vs. Disaggregate data
    • Explain - #Number of Rows
    • Basic Functions (String/Date/Numbers etc)
    • Usage of Logical conditions
  • Table calculations
    • Explain scope and direction
    • Percent of Total, Running / Cumulative calculations
    • Introduction to LOD (Level of Detail) Expressions
    • User applications of Table calculation
  • Parameters
    • Using Parameters in calculated fields
    • Bins/Reference Lines
    • Filters/Sets
    • Display Options (Dynamic Dimension/Measure Selection)
    • Create What-If/ Scenario analysis
  • Building Interactive Dashboards
    • Combining multiple visualizations into a dashboard (overview)
    • Making your worksheet interactive by using actions
    • Filter/URL/Highlight
    • Complete Interactive Dashboard for Sales & Services
  • Building Stories
    • Story Points
    • Options in Formatting your Visualization
    • Working with Labels and Annotations
    • Effective Use of Titles and Captions
  • Working with Data
    • Multiple Table Join
    • Data Blending
    • Difference between joining and blending data, and when we should do each
    • Toggle between to Direct Connection and Extracts
  • Sharing work with others
    • Sharing Workbooks
    • Publish to Reader/PDF
    • Publish to Tableau Server and sharing on the web

  • Data Importing/Exporting
    • Introduction R/R-Studio - GUI
    • Concept of Packages - Useful Packages (Base & Other packages)
    • Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
    • Importing Data from various sources
    • Exporting Data to various formats
    • Viewing Data (Viewing partial data and full data)
    • Variable & Value Labels –  Date Values
  • Data Manipulation
    • Creating New Variables (calculations & Binning)
      Dummy variable creation
    • Applying transformations
    • Handling duplicates/missing's
    • Sorting and Filtering
    • Sub setting (Rows/Columns)
    • Appending (Row/column appending)
    • Merging/Joining (Left,right,inner,full,outer)
    • Data type conversions
    • Renaming
    • Formatting
    • Reshaping data
    • Sampling
    • Operators
    • Control Structures (if, if else)
    • Loops (Conditional, iterative loops)
    • Apply functions
    • Arrays
    • R Built-in Functions
    • Text, Numeric, Date, utility
    • R User Defined Functions
    • Aggregation/Summarization
  • Data Analysis
    • Introduction exploratory data analysis
    • Descriptive statistics, Frequency Tables and summarization
    • Uni-variate Analysis (Distribution of data)
    • Bivariate Analysis(Cross Tabs, Distributions & Relationships)
  • Using R with Databases
    • R and Relational Databases
    • Connecting to Relational Databases using RJDBC and RODBC
    • Database Design and Querying Data
    • Modifying Data and Using Stored Procedures
    • In-Database Analytics with R
  • Data Visualization with R
    • Basic Visualization Tools
      • Bar Charts/Histograms/Pie Charts
      • Scatter Plots
      • Line Plots and Regression
    • Specialized Visualization Tools
      • Word Clouds/ Radar Charts
      • Waffle Charts/ Box Plots
    • How to create Maps
      • Creating Maps in R
    • How to build interactive web pages
      • Introduction to Shiny
      • Creating and Customizing Shiny Apps
      • Additional Shiny Features

  • Python Essentials (Core)
    • Overview of Python- Starting with Python
    • Why Python for data science?
    • Anaconda vs. python
    • Introduction to installation of Python
    • Introduction to Python IDE's(Jupyter,/Ipython)
    • Concept of Packages - Important packages
      NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
    • Installing & loading Packages & Name Spaces
    • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
    • List and Dictionary Comprehensions
    • Variable & Value Labels – Date & Time Values
    • Basic Operations – Mathematical/string/date
    • Control flow & conditional statements
    • Debugging & Code profiling
    • Python Built-in Functions (Text, numeric, date, utility functions)
    • User defined functions – Lambda functions
    • Concept of apply functions
    • Python – Objects – OOPs concepts
    • How to create & call class and modules?
  • Operations with NumPy (Numerical Python)
    • What is NumPy?
    • Overview of functions & methods in NumPy
    • Data structures in NumPy
    • Creating arrays and initializing
    • Reading arrays from files
    • Special initializing functions
    • Slicing and indexing
    • Reshaping arrays
    • Combining arrays
    • NumPy Maths
  • Overview of Pandas
    • What is pandas, its functions & methods
    • Pandas Data Structures (Series & Data Frames)
    • Creating Data Structures (Data import – reading into pandas)
  • Cleansing Data with Python
    • Understand the data
    • Sub Setting / Filtering / Slicing Data
      • Using [] brackets
      • Using indexing or referring with column names/rows
      • Using functions
      • Dropping rows & columns
      • Mutation of table (Adding/deleting columns)
    • Binning data (Binning numerical variables in to categorical variables)
    • Renaming columns or rows
    • Sorting (by data/values, index)  -By one column or multiple columns  - Ascending or Descending
    • Type conversions
    • Setting index
    • Handling duplicates /missing/Outliers
    • Creating dummies from categorical data (using get_dummies())
    • Applying functions to all the variables in a data frame (broadcasting)
    • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)
  • Data Analysis using Python
    • Exploratory data analysis
    • Descriptive statistics, Frequency Tables and summarization
    • Uni-variate Analysis (Distribution of data & Graphical Analysis)
    • Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Data Visualization with Python
    • Introduction to Data Visualization
    • Introduction to Matplotlib
    • Basic Plotting with Matplotlib
    • Line Plots
  • Basic Visualization Tools
    • Area Plots
    • Histograms
    • Bar Charts
    • Pie Charts
    • Box Plots
    • Scatter Plots
    • Bubble Plots
  • Advanced Visualization Tools
    • Waffle Charts
    • Word Clouds
    • Seaborn and Regression Plots
  • Visualizing Geospatial Data
    • Introduction to Folium
    • Maps with Markers
    • Choropleth Maps
  • Statistical Methods & Hypothesis Testing
    • Descriptive vs. Inferential Statistics
    • What is probability distribution?
    • Important distributions (discrete & continuous distributions)
    • Deep dive of normal distributions and properties
    • Concept of sampling & types of sampling
    • Concept of standard error and central limit theorem
    • Concept of Hypothesis Testing
    • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square

  • Introduction to Predictive Modeling
    • Concept of model in analytics and how it is used
    • Common terminology used in modeling process
    • Types of Business problems - Mapping of Algorithms
    • Different Phases of Predictive Modeling
    • Data Exploration for modeling
    • Exploring the data and identifying any problems with the data (Data Audit Report)
    • Identify missing/Outliers in the data
    • Visualize the data trends and patterns
  • Supervised Learning: Regression problems
    • Linear Regression
    • Non-linear Regression
    • K-Nearest Neighbor
    • Decision Trees
    • Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
    • Support Vector Regressor
  • Supervised Learning: Classification problems
    • Logistic Regression
    • K-Nearest Neighbor
    • Naïve Bayes Classifier
    • Decision Trees
    • Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
    • Support Vector Classifie

  • Introduction to Machine Learning
    • Applications of Machine Learning
    • Supervised vs Unsupervised Learning
    • Overall process of executing the ML project
    • Stages of ML Project
    • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
    • Concept of feature engineering
    • Regularization (LASSO, Elastic net and Ridge)
    • Types of Cross validation(Train & Test, K-Fold validation etc.)
    • Concept of optimization - Gradient descent algorithm
    • Cost & optimization functions
    • Python libraries suitable for Machine Learning
  • Unsupervised Learning
    • Principle Component Analysis 
    • K-Means Clustering
    • Hierarchical Clustering
    • Density-Based Clustering
  • Recommender Systems
    • Content-based recommender systems
    • Collaborative Filtering
  • Time Series Forecasting
    • What is forecasting?
    • Applications of forecasting
    • Time Series Components and Decomposition
    • Types of Seasonality
    • Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
    • Classification of Time Series Techniques (Uni-variate & Multivariate)
    • Time Series Modeling & Forecasting Techniques
      • Averages (Moving average, Weighted Moving Average)
      • ETS models (Holt Winter Methods)
      • Seasonal Decomposition
      • ARIMA/ARIMAX/SARIMA/SARIMAX
      • Regression
      • Evaluation of Forecasting Models

  • Introduction to Text Mining
    • Text Mining - characteristics, trends
    • Text Processing using Base Python & Pandas, Regular Expressions
      • Text processing using string functions & methods
      • Understanding regular expressions
      • Identifying patterns in the text using regular expressions  
  • Text Processing with modules like NLTK, sklearn
    • Getting Started with NLTK
    • Introduction to NLP & NLTK
    • Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)
  • Initial data processing and simple statistical tools
    • Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
    • Cleaning and normalization of data
    • Sentence Tokenize and Word Tokenize, Removing insignificant words(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatization /chunking
    • Creating Term-Document matrix
    • Tagging text with parts of speech
    • Word Sense Disambiguation
    • Finding associations
    • Measurement of similarity between documents and terms
    • Visualization of term significance in the form of word clouds
  • Advanced data processing and visualization
    • Vectorization (Count, TF-IDF, Word Embedding's)
    • Sentiment analysis (vocabulary approach, based on Bayesian probability methods)
    • Name entity recognition (NER)
    • Methods of data visualization
      • word length counts plot
      • word frequency plots
      • word clouds
      • correlation plots
      • letter frequency plot
      • Heat map
    • Grouping texts using different methods
    • Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)
  • Text Mining – Predictive Modeling
    • Semantic similarity between texts
    • Text Segmentation
    • Topic Mining (LDA)
    • Text Classification (spam detection, sentiment analysis, Intent Analysis)

  • Modern era of AI
  • Role of Machine learning & Deep Learning in AI
  • Hardware for AI (CPU vs. GPU vs. FPGA)
  • Software Frameworks for AI & Deep Learning
  • Key Industry applications of AI

  • What are the Limitations of Machine Learning?
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Overview of important python packages for Deep Learning

  • Overview of Neural Networks
  • Activation Functions, hidden layers, hidden units
  • Illustrate & Training a Perceptron
  • Important Parameters of Perceptron
  • Understand limitations of A Single Layer Perceptron
  • Illustrate Multi-Layer Perceptron
  • Understand Backpropagation – Using Example
  • Implementation of ANN in Python- Keras

  • What is Cloud Computing? Why it matters?
  • Traditional IT Infrastructure vs. Cloud Infrastructure
  • Cloud Companies (Microsoft Azure, GCP, AWS ) & their Cloud Services (Compute, storage, networking, apps, cognitive etc.)
  • Use Cases of Cloud computing
  • Overview of Cloud Segments: IaaS, PaaS, SaaS
  • Overview of Cloud Deployment Models
  • Overview of Cloud Security
  • AWS vs. Azure vs. GCP
  • Implementation of ML/DL model in Cloud

I was a fresher engineering grad & already had an offer from a renowned company, but i wanted an analytics profile, so i decided to get trained & dint go for job. After studying various alternatives, I joined ALabs for an online combo course( Visualization + Data Science). I completely agree to other reviews, i had great experience learning here, especially from Ankita mam & Sumeet Sir. The contents are well layered with exercises, assignments( with bonus questions !!) & real-world case studies. Besides these, discussions on industry best practices relevant to each topic is a common talk. Also, the well managed LMS facility eases learning & access to materials at whatever time we need. I would recommend ALabs to anyone interested in analytics with/without technical background.


Pooja Upadhay
(Student)

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