Data Science Course Pathanamthitta

Data Science Course Pathanamthitta In today’s day and age, more and more companies are seeing the value in data-driven technologies, such as artificial intelligence and automation. As such, the need for highly skilled and qualified data scientists has only continued to go up. As a matter of fact, according to statistics from IBM, the demand for data scientists will increase 28% by the year 2020.

SMEClabs Data Science Course Pathanamthitta is very beginner-friendly. Data Science Course is a fully-functional programming language that can do anything almost any other language can do, at comparable speeds. Python is capable of threading and GPU processing just like any other language. Most of the data processing modules are actually just Python wrappers around C/C++ code. Thus, Python can be used to make games, do data analysis, control robots and hardware, create GUIs, or even to create websites. Django, Flask, Rest framework are used in Python.

Data Science Course Kottayam

Data Science Course Training

Data Science Course Pathanamthitta If you’re looking for a way to jump start your career, earning your data science certification is an important step to take.

Even if you’re already experienced in data science, a professional certification from an advanced data science course can still help you grow in your career, stand out amongst the competition, and even increase your earning potential. In fact, Business Wire revealed through a study that professionals typically saw a salary increase ranging anywhere from 20% to 40% after getting certified.

In reality, just about every industry and company these days is recognizing the importance of data and the need for qualified data scientists.

Shareable Certificate

International & National Level Certification.

Online Data Science Course

Start instantly and learn at your own schedule,Data Science Course, Quick to become a professional.

Classroom Data Science Course

Get Data Science Course in Classroom at limited locations Kochi Chennai, Trivandrum, Mumbai, Calicut, Bangalore, Mangalore, Vizag, Dubai, Saudi Arabia, Qatar, Oman, Kuwait, Nigeria.

Practical only subscription

Subscription for remote lab connectivity. 24x7

Flexible Schedule

Set and maintain flexible deadlines.

Syllabus for Data Science Course

  • Numpy: Machine Learning & Scientific Computing
  • Pandas: Real-World Data Analysis
  • SciPy: Machine Learning & Scientific Computing
  • Theano: ML & DL
  • Scikit-learn: ML & Data Mining
  • Apache Spark: Next-Generation Big Data Framework
  • Natural Language Processing:NLP
  • R Programming:Data Science
  • MongoDB: NoSQL
  • Cassandra: NoSQL
  • Hadoop: Hadoop Architecture & HDFS
  • Hadoop: Distributed Processing MapReduce Framework
  • Hadoop: HBase
  • Hadoop: Hive
  • Hadoop: Apache Pig
  • Hadoop: Sqoop
  • Kafka: Distributed Streaming
  • Scala: Functional Programming
  • Apache Spark: Next-Generation Big Data Framework

Data Science Course Requirements:

Data Science Course Pathanamthitta

Data Science Course One of the best things about pursuing a certification in data science? The majority of data science courses these days are taught online. This means it’s more convenient than ever to learn a new skill and get certified. Online classes offer a level flexibility that no other method of learning provides. Data Science Course Pathanamthitta You can work at your own pace, study when you want, and pick a course schedule that best suits your other commitments. You can also complete your coursework on any supported device with a stable internet connection, anywhere in the world.

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Data Science Course Pathanamthitta

Facilities Course Curriculum Overview

  • Introduction Deep Learning
  • Life Cycle of Deep Learning
  • Skills required for Deep Learning
  • Careers Path in Deep Learning
  • Applications of Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
  • Introduction to Data:
    • Data types
    • Data Collection Techniques
  • Descriptive Statistics:
    • Measures of Central Tendency
    • Measures of Dispersion
    • Measures of Skewness and Kurtosis
    • Visualization
  • Inferential Statistics:
    • Sampling variability and Central Limit Theorem
    • Confidence Interval for Mean
    • Hypothesis ,t- Test,F-Test,Chi-square Test
    • ANOVA
  • Random Sampling and Probability Distribution:
    • Probability and Limitations,Discrete Probability,Continuous Probability
    • Binomial, Poisson Distributions,Normal Distribution
  • Environment Setup
  • Jupyter Notebook Overview
  • Data types:Numbers,Strings,Printing,Lists,Dictionaries,Booleans,Tuples ,Sets
  • Comparison Operators
  • if,elif, else Statements
  • Loops:for Loops,while Loops
  • range()
  • list comprehension
  • functions
  • lambda expressions
  • map and filter
  • methods
  • Programming Exercises.
  • Object-Oriented Programming
  • Modules and packages
  • Errors and Exception Handling
  • Python Decorators
  • Python generators
  • Collections
  • Regular Expression

Python for Exploratory Data Analysis – NumPy

  • Installing numpy
  • Using numpy
  • NumPy arrays
  • Creating numpy arrays from python list
  • Creating arrays using built in methods(arrange(),zeros(),ones(),linspace(),eye(),rand(),etc.
  • Array attributes :shape, type
  • Array methods: Reshape(),min(),max(),argmax(),argmin(),etc.

Python for Exploratory Data Analysis – Pandas

  • Introduction to Pandas
  • Series
  • DataFrames
  • Missing Data
  • GroupBy
  • Merging, Joining and Concatenating
  • Operations
  • Data Input and Output

Python for Data Visualization – Matplotlib

  • Installing Matplotlib,Basic Matplotlib commands
  • Creating Multiplot on same canvas
  • Object Oriented Method:figure(),plot(),add_axes(),subplots(),etc.
  • MatplotlibExercise

Python for Data Visualization – Seaborn and Panda

  • Categorical plot
  • Distribution plot
  • Regression plot
  • Seaborn Exercise
  • Installation
  • Creating Your First Graph and Running It in a Session
  • Managing Graphs
  • Lifecycle of a Node Value
  • Linear Regression with TensorFlow
  • Implementing Gradient Descent
  • Feeding Data to the Training Algorithm
  • Saving and Restoring Models
  • Visualizing the Graph and Training Curves Using TensorBoard
  • Name Scopes, Modularity
  • Sharing Variables
  • From Biological to Artificial Neurons
  • Training an MLP with TensorFlow’s High-Level API
  • Training a DNN Using Plain TensorFlow
  • Fine-Tuning Neural Network Hyperparameters
  • Vanishing / Exploding Gradients Problems
  • Reusing Pretrained Layers
  • Faster Optimizers
  • Avoiding Overfitting Through Regularization
  • Practical Guidelines
  • The Architecture of the Visual Cortex
  • Convolutional Layer
  • Pooling Layer
  • CNN Architectures
  • Recurrent Neurons
  • Basic RNNs in TensorFlow
  • Training RNNs, Deep RNNs
  • LSTM Cell
  • GRU Cell
  • Natural Language Processing
  • Efficient Data Representations
  • Performing PCA with an Undercomplete Linear Autoencoder
  • Stacked Autoencoders
  • Unsupervised Pretraining Using Stacked Autoencoders
  • DenoisingAutoencoders
  • Sparse Autoencoders
  • Variational Autoencoders
  • Learning to Optimize RewardsPolicy Search
  • Introduction to OpenAI Gym
  • Neural Network Policies
  • Evaluating Actions: The Credit Assignment Problem, Policy Gradients, Markov Decision Processes, Temporal Difference Learning and Q-Learning, Learning to Play Ms. Pac-Man Using Deep Q-Learning

Keras API

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras


  • Define TFLearn
  • Composing Models in TFLearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFLearn
  • Customizing the Training Process
  • Using TensorBoard with TFLearn
  • Use-Case Implementation with TFLearn

Programming Languages and Tools Covered

Data Science Course Pathanamthitta
Data Science Course Pathanamthitta
Data Science Course Pathanamthitta
Data Science Course Pathanamthitta
Data Science Course Pathanamthitta
Data Science Course Pathanamthitta
Data Science Course Pathanamthitta
Data Science Course Pathanamthitta

FAQ - Frequently Asked Questions

The data science course field is growing quickly, and more employers are recognizing the value in those skilled in data science. In fact, has reported that job postings for data scientists increased by 80% over a recent three-year period. Although the demand for data scientists is undeniably high, so is the competition.

Data Science Course Pathanamthitta, SMEClabs Provides Best futuristic demands of the new age career trends, Data Science and Business Analytics Training, Business intelligence & analytics training
Data is the key engine in today’s economy. Every business is making use of the data’s power to increase the efficiency of their operations. Data Science has become one of the most sought-after careers in recent years, providing a wide range of opportunities in the area that deal with data sciences. According to data from the U.S. Bureau of Labor Statistics the knowledge that experts in data science have can lead to an increase of 30% in employment by 2026. There is a high demand for skilled specialists in data science. There is a shortage highly competent data scientists as well with other specialists working in this field.
Data science is almost both an art and a science, and involves the extraction and analysis of vital data from relevant sources when it comes to measuring success and planning for future goals. Most businesses these days rely heavily on data science.