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Data Analyst

Information Technology


Provider Name : Apson

Duration(Hrs) :90

Hourse/day :2

Training Type :Virtual Session

Certification :Yes

No of Slots :650

  1. Introduction to Data Science:
  1. What is data science?
  2. Importance of data science in various industries.
  3. Overview of data science lifecycle and methodologies.
  4. Python Programming:
  1. Basics of Python programming language.
  2. Data structures (lists, tuples, dictionaries, sets).
  3. Control flow (if statements, loops).
  4. Functions and modules.
  5. File handling.
  6. Data Manipulation and Analysis with Pandas:
  1. Introduction to Pandas Library.
  2. Loading and inspecting datasets.
  3. Data cleaning and preprocessing.
  4. Data manipulation (filtering, sorting, grouping, merging).
  5. Exploratory data analysis (EDA).
  6. Data Visualization with Matplotlib and Seaborn:
  1. Introduction to data visualization.
  2. Plotting basic charts (line plots, bar plots, histograms).
  3. Customizing plots (labels, titles, colors).
  4. Advanced visualization techniques.
  5. Introduction to Machine Learning:
  1. Overview of machine learning concepts.
  2. Supervised learning vs. unsupervised learning.
  3. Model evaluation and validation.
  4. Introduction to scikit-learn library.
  5. Supervised Learning Algorithms:
  1. Linear regression.
  2. Logistic regression.
  3. Decision trees and ensemble methods (random forests, gradient boosting).
  4. Unsupervised Learning Algorithms:
  1. K-means clustering.
  2. Hierarchical clustering.
  3. Dimensionality reduction techniques (PCA).
  4. Natural Language Processing (NLP):
  1. Introduction to NLP.
  2. Text preprocessing (tokenization, stemming, lemmatization).
  3. Sentiment analysis.
  4. Named Entity Recognition (NER).
  5. Deep Learning:
  1. Introduction to neural networks.
  2. Basics of TensorFlow or PyTorch.
  3. Building and training neural network models.
  4. Convolutional Neural Networks (CNNs) for image recognition.
  5. Recurrent Neural Networks (RNNs) for sequence data.
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